Github user thunterdb commented on a diff in the pull request:
https://github.com/apache/spark/pull/10472#discussion_r48993085
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
---
@@ -79,13 +79,14 @@ class BinaryClassificationEvaluator @Since("1.4.0")
(@Since("1.4.0") override va
@Since("1.2.0")
override def evaluate(dataset: DataFrame): Double = {
val schema = dataset.schema
- SchemaUtils.checkColumnType(schema, $(rawPredictionCol), new VectorUDT)
+ SchemaUtils.checkColumnTypes(schema, $(rawPredictionCol),
Seq(DoubleType, new VectorUDT))
SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
// TODO: When dataset metadata has been implemented, check
rawPredictionCol vector length = 2.
val scoreAndLabels = dataset.select($(rawPredictionCol), $(labelCol))
- .map { case Row(rawPrediction: Vector, label: Double) =>
- (rawPrediction(1), label)
+ .map {
+ case Row(rawPrediction: Vector, label: Double) =>
(rawPrediction(1), label)
+ case Row(rawPrediction: Double, label: Double) => (rawPrediction,
label)
--- End diff --
there is a small loss of performance because a conditional branch is
introduced here, but I believe the cost of unpacking the row is much higher
anyway.
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