[GitHub] spark issue #12819: [SPARK-14077][ML] Refactor NaiveBayes to support weighte...
Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/12819 @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 ```-1, +1``` or similar labels efficiently. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/12819 @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 we may remove mllib package in later release. But I think this can be done in a separate follow-up work. @sethah Any more comments about this? Thanks. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/12819 @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). * I'm not sure the UDAF API is stable enough and have confidential performance. * This PR mainly focus on moving NB implementation from mllib to ml package and refactor it to support weighted instance. Let's focus on what it should be. We can change it to use dataset based UDAF in a separate work later if it's necessary. Thanks! --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user sethah commented on the issue: https://github.com/apache/spark/pull/12819 @yanboliang What went into the decision to use RDD based aggregation? Just curious, thanks! --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/12819 @zhengruifeng As we discussed offline, we would like to still use RDD operation underneath rather than UDAF. Please update the PR accordingly. Thanks! --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/12819
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 org.apache.spark.ml.classification.NaiveBayes
val path = "/Users/zrf/Dev/Data/mnist.scale"
val data = spark.read.format("libsvm").load(path).persist()
println(data.count)
val t0 = System.currentTimeMillis()
for(i <- 0 until 100) { println(i); val model = new NaiveBayes().fit(data) }
val t1 = System.currentTimeMillis()
println(t1-t0)
```
Result duration:
`a9a`: Spark2.0 7944; This PR 6877
`mnist.scale`: Spark2.0 11881; This PR 13287
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Github user zhengruifeng commented on the issue: https://github.com/apache/spark/pull/12819 @yanboliang I will make usage of `weightCol` keeping in line with other algorithms, and I will do some performance test. Thanks for reviewing this. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/12819 @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 performance degradation. Otherwise, we can still use the RDD operation in this PR and update it to use Dataset later. Thanks! --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yanboliang commented on the issue: https://github.com/apache/spark/pull/12819 I will make a pass soon. Thanks! --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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