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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