Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/5820#discussion_r29563744
--- Diff: mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala ---
@@ -36,5 +35,18 @@ abstract class Evaluator extends Identifiable {
* @param paramMap parameter map that specifies the input columns and
output metrics
* @return metric
*/
- def evaluate(dataset: DataFrame, paramMap: ParamMap): Double
+ def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = {
+ this.copy(paramMap).evaluate(dataset)
+ }
+
--- End diff --
One question. Why `Evaluator` returns type `Double`? For example, confusion
matrix can be an evaluator as well, and it can not be represented by type
`Double`. Also, in our use-case, we often evaluate the models by the histogram
of recommended popular titles, so it will be `Array[Long]`. Why don't we have
type `T` here, and we specify the type in the implementation?
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