Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15018#discussion_r94571308
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
---
@@ -328,74 +336,81 @@ class IsotonicRegression private (private var
isotonic: Boolean) extends Seriali
return Array.empty
}
- // Pools sub array within given bounds assigning weighted average
value to all elements.
- def pool(input: Array[(Double, Double, Double)], start: Int, end:
Int): Unit = {
- val poolSubArray = input.slice(start, end + 1)
- val weightedSum = poolSubArray.map(lp => lp._1 * lp._3).sum
- val weight = poolSubArray.map(_._3).sum
+ // Keeps track of the start and end indices of the blocks. if [i, j]
is a valid block from
+ // input(i) to input(j) (inclusive), then blockBounds(i) = j and
blockBounds(j) = i
+ val blockBounds = Array.range(0, input.length) // Initially, each data
point is its own block
- var i = start
- while (i <= end) {
- input(i) = (weightedSum / weight, input(i)._2, input(i)._3)
- i = i + 1
- }
+ // Keep track of the sum of weights and sum of weight * y for each
block. weights(start)
+ // gives the values for the block. Entries that are not at the start
of a block
+ // are meaningless.
+ val weights: Array[(Double, Double)] = input.map {
+ case (_, _, weight) if weight == 0d =>
--- End diff --
I'd always write 0.0 instead of 0d for clarity. I also think we want an
`IllegalArgumentException`, so maybe:
```
val weights = input.map { case (y, _, weight) =>
require(weight > 0.0)
(weight, weight * y)
}
```
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