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[SPARK-23152][ML] - Correctly guard against empty datasets #20321

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[SPARK-23152][ML] - Correctly guard against empty datasets #20321

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bf0efe1
Correctly guard against empty datasets
tovbinm Jan 18, 2018
68b3584
Added a test to verify the guard condition
tovbinm Jan 19, 2018
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
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Expand Up @@ -109,7 +109,7 @@ abstract class Classifier[
case None =>
// Get number of classes from dataset itself.
val maxLabelRow: Array[Row] = dataset.select(max($(labelCol))).take(1)
if (maxLabelRow.isEmpty) {
if (maxLabelRow.isEmpty || maxLabelRow(0).get(0) == null) {
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Hi, @tovbinm .
If this is a correctness issue, could you add a new test case for your new contribution?

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@dongjoon-hyun done.

throw new SparkException("ML algorithm was given empty dataset.")
}
val maxDoubleLabel: Double = maxLabelRow.head.getDouble(0)
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7 changes: 7 additions & 0 deletions mllib/src/test/scala/org/apache/spark/ml/classification/ClassifierSuite.scala
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Expand Up @@ -90,6 +90,13 @@ class ClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
}
assert(e.getMessage.contains("requires integers in range"))
}
val df3 = getTestData(Seq.empty[Double])
withClue("getNumClasses should fail if dataset is empty") {
val e: SparkException = intercept[SparkException] {
c.getNumClasses(df3)
}
assert(e.getMessage == "ML algorithm was given empty dataset.")
}
}
}

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