Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22764#discussion_r226812121
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/BisectingKMeansModel.scala
---
@@ -225,13 +227,14 @@ object BisectingKMeansModel extends
Loader[BisectingKMeansModel] {
assert(formatVersion == thisFormatVersion)
val rootId = (metadata \ "rootId").extract[Int]
val distanceMeasure = (metadata \ "distanceMeasure").extract[String]
+ val trainingCost = (metadata \ "trainingCost").extract[Double]
--- End diff --
@mgaido91
What about this way ?
In `ml` reader, we can load `trainingCost` and then construct
`mllib.clustering.BisectingKMeansModel` and pass the `trainingCost` argument
and then construct `ml.clustering.BisectingKMeansModel` to wrap that `mllib`
model.
** Then in `ml` reader we can check the spark major/minor version to keep
backwards compatibility, this is a more important thing. **
** In `mllib` loader we can ignore loading `trainingCost`, because `mllib`
is deprecate, we can ignore adding new features, but we cannot breaking
backwards compatibility (your change breaking backwards compatibility). **
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