Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/14321#discussion_r72415601
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala ---
@@ -98,7 +105,21 @@ class TrainValidationSplit @Since("1.5.0")
(@Since("1.5.0") override val uid: St
val metrics = new Array[Double](epm.length)
val Array(trainingDataset, validationDataset) =
- dataset.randomSplit(Array($(trainRatio), 1 - $(trainRatio)), $(seed))
+ if ($(stratifiedCol).nonEmpty) {
+ val stratifiedColIndex =
schema.fieldNames.indexOf($(stratifiedCol))
+ val pairData = dataset.toDF.rdd.map(row =>
(row(stratifiedColIndex), row))
+ val keys = pairData.keys.distinct.collect()
+ val weights: Array[scala.collection.Map[Any, Double]] =
+ Array(keys.map((_, $(trainRatio))).toMap, keys.map((_, 1 -
$(trainRatio))).toMap)
+ val splitsWithKeys = pairData.randomSplitByKey(weights, exact =
true, $(seed))
--- End diff --
does it make sense perhaps to have a convenience version of
`randomSplitByKey` that takes an `Array[Double]` for weights and applies the
same sampling weight for each key? Since I would expect the vast majority of
the time the use case is to split the dataset into folds with the same sampling
ratio across keys?
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