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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2016/04/08 21:37:25 UTC
[jira] [Commented] (SPARK-10086) Flaky StreamingKMeans test in
PySpark
[ https://issues.apache.org/jira/browse/SPARK-10086?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15232777#comment-15232777 ]
Joseph K. Bradley commented on SPARK-10086:
-------------------------------------------
I'm removing the target version. This is an important issue, but I don't see us getting a fix in for 2.0---and I have not heard of users encountering it (though the source of this bug might be hard to discern in practice). Let's revisit for the next release.
> Flaky StreamingKMeans test in PySpark
> -------------------------------------
>
> Key: SPARK-10086
> URL: https://issues.apache.org/jira/browse/SPARK-10086
> Project: Spark
> Issue Type: Bug
> Components: MLlib, PySpark, Streaming, Tests
> Affects Versions: 1.5.0
> Reporter: Joseph K. Bradley
> Priority: Critical
> Attachments: flakyRepro.py
>
>
> Here's a report on investigating test failures in StreamingKMeans in PySpark. (See Jenkins links below.)
> It is a StreamingKMeans test which trains on a DStream with 2 batches and then tests on those same 2 batches. It fails here: [https://github.com/apache/spark/blob/1968276af0f681fe51328b7dd795bd21724a5441/python/pyspark/mllib/tests.py#L1144]
> I recreated the same test, with variants training on: (1) the original 2 batches, (2) just the first batch, (3) just the second batch, and (4) neither batch. Here is code which avoids Streaming altogether to identify what batches were processed.
> {code}
> from pyspark.mllib.clustering import StreamingKMeans, StreamingKMeansModel
> batches = [[[-0.5], [0.6], [0.8]], [[0.2], [-0.1], [0.3]]]
> batches = [sc.parallelize(batch) for batch in batches]
> stkm = StreamingKMeans(decayFactor=0.0, k=2)
> stkm.setInitialCenters([[0.0], [1.0]], [1.0, 1.0])
> # Train
> def update(rdd):
> stkm._model.update(rdd, stkm._decayFactor, stkm._timeUnit)
> # Remove one or both of these lines to test skipping batches.
> update(batches[0])
> update(batches[1])
> # Test
> def predict(rdd):
> return stkm._model.predict(rdd)
> predict(batches[0]).collect()
> predict(batches[1]).collect()
> {code}
> *Results*:
> {code}
> ####################### EXPECTED
> [0, 1, 1]
> [1, 0, 1]
> ####################### Skip batch 0
> [1, 0, 0]
> [0, 1, 0]
> ####################### Skip batch 1
> [0, 1, 1]
> [1, 0, 1]
> ####################### Skip both batches (This is what we see in the test failures.)
> [0, 1, 1]
> [0, 0, 0]
> {code}
> Skipping both batches reproduces the failure. There is no randomness in the StreamingKMeans algorithm (since initial centers are fixed, not randomized).
> CC: [~tdas] [~freeman-lab] [~mengxr]
> Failure message:
> {code}
> ======================================================================
> FAIL: test_trainOn_predictOn (__main__.StreamingKMeansTest)
> Test that prediction happens on the updated model.
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File "/home/jenkins/workspace/SparkPullRequestBuilder@3/python/pyspark/mllib/tests.py", line 1147, in test_trainOn_predictOn
> self._eventually(condition, catch_assertions=True)
> File "/home/jenkins/workspace/SparkPullRequestBuilder@3/python/pyspark/mllib/tests.py", line 123, in _eventually
> raise lastValue
> File "/home/jenkins/workspace/SparkPullRequestBuilder@3/python/pyspark/mllib/tests.py", line 114, in _eventually
> lastValue = condition()
> File "/home/jenkins/workspace/SparkPullRequestBuilder@3/python/pyspark/mllib/tests.py", line 1144, in condition
> self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
> AssertionError: Lists differ: [[0, 1, 1], [0, 0, 0]] != [[0, 1, 1], [1, 0, 1]]
> First differing element 1:
> [0, 0, 0]
> [1, 0, 1]
> - [[0, 1, 1], [0, 0, 0]]
> ? ^^^^
> + [[0, 1, 1], [1, 0, 1]]
> ? +++ ^
> ----------------------------------------------------------------------
> Ran 62 tests in 164.188s
> {code}
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