Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/16699#discussion_r98013552
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
@@ -263,17 +288,21 @@ class GeneralizedLinearRegression @Since("2.0.0")
(@Since("2.0.0") override val
}
val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0)
else col($(weightCol))
- val instances: RDD[Instance] =
- dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
- case Row(label: Double, weight: Double, features: Vector) =>
- Instance(label, weight, features)
+ val off = if (!isDefined(offsetCol) || $(offsetCol).isEmpty) lit(0.0)
else col($(offsetCol))
+ val instances: RDD[OffsetInstance] =
+ dataset.select(col($(labelCol)), w, off,
col($(featuresCol))).rdd.map {
+ case Row(label: Double, weight: Double, offset: Double, features:
Vector) =>
+ OffsetInstance(label, weight, offset, features)
}
val model = if (familyObj == Gaussian && linkObj == Identity) {
// TODO: Make standardizeFeatures and standardizeLabel configurable.
+ val wlsInstances: RDD[Instance] = instances.map { instance =>
+ Instance(instance.label - instance.offset, instance.weight,
instance.features)
--- End diff --
another thing you can do, if you don't want to change the hierarchy, is to
move the initialization of instances inside the if/else. Then, for
weightedleastsquares, you can just create RDD[Instance], but for IRLS weighted
instances.
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