Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/4863#discussion_r25714389
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
---
@@ -53,6 +54,13 @@ class BinaryClassificationMetrics(
*/
def this(scoreAndLabels: RDD[(Double, Double)]) = this(scoreAndLabels, 0)
+ /**
+ * An auxiliary constructor taking a DataFrame.
+ * @param scoreAndLabels a DataFrame with two double columns: score and
label
+ */
+ private[mllib] def this(scoreAndLabels: DataFrame) =
+ this(scoreAndLabels.map(r => (r.getDouble(0), r.getDouble(1))))
--- End diff --
In the past, MLLib did not depend on SQL, but if we're going to stick with
DataFrame, it's fine to use 'toDF'.
Both of them (RDD[Row] and RDD[Vector]) will use pickle for serialization,
so maybe they will not have much difference.
Let's keep this.
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