Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/19156#discussion_r149928022 --- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala --- @@ -94,46 +98,87 @@ object Summarizer extends Logging { * - min: the minimum for each coefficient. * - normL2: the Euclidian norm for each coefficient. * - normL1: the L1 norm of each coefficient (sum of the absolute values). - * @param firstMetric the metric being provided - * @param metrics additional metrics that can be provided. + * @param metrics metrics that can be provided. * @return a builder. * @throws IllegalArgumentException if one of the metric names is not understood. * * Note: Currently, the performance of this interface is about 2x~3x slower then using the RDD * interface. */ @Since("2.3.0") - def metrics(firstMetric: String, metrics: String*): SummaryBuilder = { - val (typedMetrics, computeMetrics) = getRelevantMetrics(Seq(firstMetric) ++ metrics) + @scala.annotation.varargs + def metrics(metrics: String*): SummaryBuilder = { --- End diff -- How about binary compatibility? e.g. spark jobs built with old spark versions, can they run on new Spark without re-compile?
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