Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/19020#discussion_r134476397
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -344,33 +408,58 @@ class LinearRegression @Since("1.3.0")
(@Since("1.3.0") override val uid: String
} else {
None
}
- val costFun = new RDDLossFunction(instances, getAggregatorFunc,
regularization,
- $(aggregationDepth))
- val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam ==
0.0) {
- new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
- } else {
- val standardizationParam = $(standardization)
- def effectiveL1RegFun = (index: Int) => {
- if (standardizationParam) {
- effectiveL1RegParam
+ val costFun = $(loss) match {
+ case LeastSquares =>
+ val getAggregatorFunc = new LeastSquaresAggregator(yStd, yMean,
$(fitIntercept),
+ bcFeaturesStd, bcFeaturesMean)(_)
+ new RDDLossFunction(instances, getAggregatorFunc, regularization,
$(aggregationDepth))
+ case Huber =>
+ val getAggregatorFunc = new HuberAggregator($(fitIntercept), $(m),
bcFeaturesStd)(_)
+ new RDDLossFunction(instances, getAggregatorFunc, regularization,
$(aggregationDepth))
--- End diff --
I think no. I was trying to factored it out, but because
```LeastSquaresAggregator``` and ```HuberAggregator``` pass in the type of
itself, so the compile will complain. Maybe @sethah can give some suggestion?
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