From 71ad945bbbdd154eae852cd7f841e98f7a83e8d4 Mon Sep 17 00:00:00 2001 From: z001qdp Date: Fri, 15 Jul 2016 12:30:22 +0100 Subject: [SPARK-16426][MLLIB] Fix bug that caused NaNs in IsotonicRegression ## What changes were proposed in this pull request? Fixed a bug that caused `NaN`s in `IsotonicRegression`. The problem occurs when training rows with the same feature value but different labels end up on different partitions. This patch changes a `sortBy` call to a `partitionBy(RangePartitioner)` followed by a `mapPartitions(sortBy)` in order to ensure that all rows with the same feature value end up on the same partition. ## How was this patch tested? Added a unit test. Author: z001qdp Closes #14140 from neggert/SPARK-16426-isotonic-nan. --- .../apache/spark/mllib/regression/IsotonicRegression.scala | 9 ++++++--- .../spark/mllib/regression/IsotonicRegressionSuite.scala | 11 +++++++++++ 2 files changed, 17 insertions(+), 3 deletions(-) (limited to 'mllib') diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala index 1cd6f2a896..377326f873 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala @@ -35,6 +35,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD import org.apache.spark.sql.SparkSession +import org.apache.spark.RangePartitioner /** * Regression model for isotonic regression. @@ -408,9 +409,11 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali */ private def parallelPoolAdjacentViolators( input: RDD[(Double, Double, Double)]): Array[(Double, Double, Double)] = { - val parallelStepResult = input - .sortBy(x => (x._2, x._1)) - .glom() + val keyedInput = input.keyBy(_._2) + val parallelStepResult = keyedInput + .partitionBy(new RangePartitioner(keyedInput.getNumPartitions, keyedInput)) + .values + .mapPartitions(p => Iterator(p.toArray.sortBy(x => (x._2, x._1)))) .flatMap(poolAdjacentViolators) .collect() .sortBy(x => (x._2, x._1)) // Sort again because collect() doesn't promise ordering. diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala index ea4f286575..94da626d92 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala @@ -176,6 +176,17 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w assert(model.predictions === Array(1, 2, 2)) } + test("SPARK-16426 isotonic regression with duplicate features that produce NaNs") { + val trainRDD = sc.parallelize(Seq[(Double, Double, Double)]((2, 1, 1), (1, 1, 1), (0, 2, 1), + (1, 2, 1), (0.5, 3, 1), (0, 3, 1)), + 2) + + val model = new IsotonicRegression().run(trainRDD) + + assert(model.boundaries === Array(1.0, 3.0)) + assert(model.predictions === Array(0.75, 0.75)) + } + test("isotonic regression prediction") { val model = runIsotonicRegression(Seq(1, 2, 7, 1, 2), true) -- cgit v1.2.3