srowen commented on a change in pull request #24717: [SPARK-27847][ML] One-Pass
MultilabelMetrics & MulticlassMetrics
URL: https://github.com/apache/spark/pull/24717#discussion_r288596255
##########
File path:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
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@@ -142,17 +124,17 @@ class MultilabelMetrics @Since("1.2.0")
(predictionAndLabels: RDD[(Array[Double]
if((p + r) == 0) 0.0 else 2 * p * r / (p + r)
}
- private lazy val sumTp = tpPerClass.foldLeft(0L) { case (sum, (_, tp)) =>
sum + tp }
- private lazy val sumFpClass = fpPerClass.foldLeft(0L) { case (sum, (_, fp))
=> sum + fp }
- private lazy val sumFnClass = fnPerClass.foldLeft(0L) { case (sum, (_, fn))
=> sum + fn }
+ private lazy val sumTp = summary.tpPerClass.values.sum
+ private lazy val sumFpClass = summary.fpPerClass.values.sum
+ private lazy val sumFnClass = summary.fnPerClass.values.sum
/**
* Returns micro-averaged label-based precision
* (equals to micro-averaged document-based precision)
*/
@Since("1.2.0")
lazy val microPrecision: Double = {
- val sumFp = fpPerClass.foldLeft(0L) { case(cum, (_, fp)) => cum + fp}
+ val sumFp = summary.fpPerClass.foldLeft(0L) { case(cum, (_, fp)) => cum +
fp}
Review comment:
Same point about .values.sum here I think
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