Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/6386#discussion_r30955132
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -106,6 +106,11 @@ class LogisticRegression(override val uid: String)
case LabeledPoint(label: Double, features: Vector) => (label,
features)
}
val handlePersistence = dataset.rdd.getStorageLevel ==
StorageLevel.NONE
+ trainOnInstances(instances, handlePersistence)
+ }
+
+ protected[spark] def trainOnInstances(instances: RDD[(Double, Vector)],
--- End diff --
That an option, I figured it would be better to not round trip it through a
`DataFrame` since we would have to create a `SQLContext` and the `ml`
implementation just rips out the `LabeledPoint`s from the DataFrame as soon as
its passed in - but I can do it this way if that would be better :)
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