Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7884#discussion_r39474414
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -218,31 +228,48 @@ class LogisticRegression(override val uid: String)
override def getThreshold: Double = super.getThreshold
+ /**
+ * Whether to over-/under-sample training instances according to the
given weights in weightCol.
+ * If empty, all instances are treated equally (weight 1.0).
+ * Default is empty, so all instances have weight one.
+ * @group setParam
+ */
+ def setWeightCol(value: String): this.type = set(weightCol, value)
+ setDefault(weightCol -> "")
+
override def setThresholds(value: Array[Double]): this.type =
super.setThresholds(value)
override def getThresholds: Array[Double] = super.getThresholds
override protected def train(dataset: DataFrame):
LogisticRegressionModel = {
// Extract columns from data. If dataset is persisted, do not persist
oldDataset.
- val instances = extractLabeledPoints(dataset).map {
- case LabeledPoint(label: Double, features: Vector) => (label,
features)
+ val instances: RDD[Instance] = if ($(weightCol).isEmpty) {
--- End diff --
From my previous comment, using `val w = if ($(weightCol).isEmpty) lit(1.0)
else col($(weightCol))` could simplify this block.
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