uchiiii commented on code in PR #36843:
URL: https://github.com/apache/spark/pull/36843#discussion_r897422167
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mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala:
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@@ -142,33 +158,57 @@ class RankingMetrics[T: ClassTag](predictionAndLabels:
RDD[(Array[T], Array[T])]
@Since("1.2.0")
def ndcgAt(k: Int): Double = {
require(k > 0, "ranking position k should be positive")
- predictionAndLabels.map { case (pred, lab) =>
- val labSet = lab.toSet
-
- if (labSet.nonEmpty) {
- val labSetSize = labSet.size
- val n = math.min(math.max(pred.length, labSetSize), k)
- var maxDcg = 0.0
- var dcg = 0.0
- var i = 0
- while (i < n) {
- // Base of the log doesn't matter for calculating NDCG,
- // if the relevance value is binary.
- val gain = 1.0 / math.log(i + 2)
- if (i < pred.length && labSet.contains(pred(i))) {
- dcg += gain
+ predictionAndLabels
+ .map {
+ case (pred, lab, rel) =>
+ val useBinary = rel.isEmpty
+ val labSet = lab.toSet
+ val relMap = (lab zip rel).toMap
Review Comment:
This is to calculate the `dcg` fast. When `dcg` is calculated, the relevance
score of pred(i) is required. (`lab` and `rel` is parallel but pred is not`)
FYI:
-
https://spark.apache.org/docs/1.5.0/mllib-evaluation-metrics.html#ranking-systems
-
https://github.com/apache/spark/pull/36843/files#diff-5b07c866965423f2ef414592a98e8d22426bd641463d35094bd1ece2656d3657R144
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