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[SPARK-15892][ML] Incorrectly merged AFTAggregator with zero total count #13619

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HyukjinKwon wants to merge 5 commits into from
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[SPARK-15892][ML] Incorrectly merged AFTAggregator with zero total count #13619

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fb16b71
Fix incorrect comparison
HyukjinKwon Jun 11, 2016
9f7bec3
Fetch upstream
HyukjinKwon Jun 12, 2016
4447d0a
Add a test
HyukjinKwon Jun 12, 2016
c86ede8
Fix typos
HyukjinKwon Jun 12, 2016
2c8adbf
Do not make 1000 AFTInput but just 2
HyukjinKwon Jun 12, 2016
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -538,7 +538,7 @@ private class AFTAggregator(
* @return This AFTAggregator object.
*/
def merge(other: AFTAggregator): this.type = {
if (totalCnt != 0) {
if (other.count != 0) {
totalCnt += other.totalCnt
lossSum += other.lossSum

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12 changes: 12 additions & 0 deletions mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
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Expand Up @@ -390,6 +390,18 @@ class AFTSurvivalRegressionSuite
testEstimatorAndModelReadWrite(aft, datasetMultivariate,
AFTSurvivalRegressionSuite.allParamSettings, checkModelData)
}

test("SPARK-15892: Incorrectly merged AFTAggregator with zero total count") {
// This `dataset` will contain an empty partition because it has two rows but
// the parallelism is bigger than that. Because the issue was about `AFTAggregator`s
// being merged incorrectly when it has an empty partition, running the codes below
// should not throw an exception.
val dataset = spark.createDataFrame(
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@zzcclp zzcclp Jun 13, 2016

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@HyukjinKwon value spark is not found here, it should be sqlContext, right?

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@zzcclp zzcclp Jun 13, 2016

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I compile it in branch-1.6.

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@zzcclp zzcclp Jun 13, 2016

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with branch-2.0, it is OK , i think that this pr should not be merged into branch-1.6 directly.

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@HyukjinKwon HyukjinKwon Jun 13, 2016

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Oh, it seems this is merged into branch-1.6 too. Yes, it should be sqlContext for branch-1.6.

sc.parallelize(generateAFTInput(
1, Array(5.5), Array(0.8), 2, 42, 1.0, 2.0, 2.0), numSlices = 3))
val trainer = new AFTSurvivalRegression()
trainer.fit(dataset)
}
}

object AFTSurvivalRegressionSuite {
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