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Posted to reviews@spark.apache.org by srowen <gi...@git.apache.org> on 2016/05/01 08:48:03 UTC
[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
GitHub user srowen opened a pull request:
https://github.com/apache/spark/pull/12821
[SPARK-15043] [MLLIB] Fix and re-enable flaky test: mllib.stat.JavaStatisticsSuite.testCorr
## What changes were proposed in this pull request?
Following https://github.com/apache/spark/pull/12779 this test became flaky. The issue is that the mean, computed for a covariance calculation, is now calculated with the standard and slightly more accurate `MultivariateOnlineSummarizer`. However I think the fact that it uses `treeAggregate` internally can lead to a different order of summation and very very slightly different results on different runs.
The immediate fix for the test, which asserts equality, is to use 1 partition. We can find out if that appears to be robust.
More generally, it's an interesting question whether we want `MultivariateOnlineSummarizer` to be deterministic. I'm not sure if another aggregation method would provide more guarantees of this, in theory or practice.
## How was this patch tested?
Existing Java stats suite.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/srowen/spark SPARK-15043
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/12821.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #12821
----
commit f755850a0bbcae48118c783a7ca643649deef327
Author: Sean Owen <so...@cloudera.com>
Date: 2016-05-01T08:43:13Z
Use 1 partition for simple Java stats test
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216030597
**[Test build #57475 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/57475/consoleFull)** for PR 12821 at commit [`f755850`](https://github.com/apache/spark/commit/f755850a0bbcae48118c783a7ca643649deef327).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216030627
Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/57475/
Test PASSed.
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216119750
@srowen If we only use 1 partition, it doesn't touch the code path of `treeAggregation`, which means the coverage is not sufficient. I think we should use multiple partitions and test equality with a small tolerance.
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by srowen <gi...@git.apache.org>.
Github user srowen closed the pull request at:
https://github.com/apache/spark/pull/12821
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216026251
**[Test build #57475 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/57475/consoleFull)** for PR 12821 at commit [`f755850`](https://github.com/apache/spark/commit/f755850a0bbcae48118c783a7ca643649deef327).
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by JoshRosen <gi...@git.apache.org>.
Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216058335
/cc @mengxr for review, since @jkbradley mentioned that you might have fixed the flakiness via a separate patch.
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216225226
Sounds good, it's either make it so simple that it's deterministic or tolerate tiny variation.
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216045431
FWIW you can consistently reproduce the differing results with ..
```
val x = sc.parallelize(Seq(1.0, 2.0, 3.0, 4.0))
val y = sc.parallelize(Seq(1.1, 2.2, 3.1, 4.3))
(0 to 10).map(i => Statistics.corr(x,y)).distinct
```
With 1 partition the result is always the same. The result is the same if I use `aggregate` instead of `treeAggregate`.
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216030626
Merged build finished. Test PASSed.
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216225537
Oh already addressed in https://github.com/apache/spark/commit/19a6d192d53ce6dffe998ce110adab1f2efcb23e
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by jkbradley <gi...@git.apache.org>.
Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216066630
This patch changed the test to use approx equality: https://github.com/apache/spark/commit/19a6d192d53ce6dffe998ce110adab1f2efcb23e
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[GitHub] spark pull request: [SPARK-15043] [MLLIB] Fix and re-enable flaky ...
Posted by jkbradley <gi...@git.apache.org>.
Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/12821#issuecomment-216066867
It would be great to have it be deterministic, but it sounds hard or impossible to ensure beyond numerical precision in general.
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