[jira] [Created] (SPARK-16426) IsotonicRegression produces NaNs with certain data

["Nic Eggert (JIRA)" <ji...@apache.org>]

Nic Eggert created SPARK-16426:
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             Summary: IsotonicRegression produces NaNs with certain data
                 Key: SPARK-16426
                 URL: https://issues.apache.org/jira/browse/SPARK-16426
             Project: Spark
          Issue Type: Bug
          Components: MLlib
    Affects Versions: 1.6.2, 1.5.2, 1.4.1, 1.3.1
            Reporter: Nic Eggert


{code:scala}
val r = 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 i = new IsotonicRegression().run(r)

scala> i.predict(3.0)
res12: Double = NaN

scala> i.predictions
res13: Array[Double] = Array(0.75, 0.75, NaN, NaN)
{code}

I believe I understand the problem so I'll submit a PR shortly.

The problem happens when rows with the same feature value but different labels end up on different partitions. The merge function in poolAdjacentViolators introduces 0-weight points to be used for linear interpolation. This works fine, as long as they are always next to a non-0-weight point, but in the above case, you can end up with two 0-weight points  with the same feature value, which end up next to each other in the final PAV step. If these points are pooled, it creates a NaN.

One solution to this is to ensure that the all points with identical feature values end up on the same partition. This is the solution I intend to submit a PR for. Another option would be to try to get rid of the 0-weight points, but that seems trickier to me.



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