Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/19020#discussion_r147326457
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -344,33 +449,58 @@ 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 SquaredError =>
+ val getAggregatorFunc = new LeastSquaresAggregator(yStd, yMean,
$(fitIntercept),
+ bcFeaturesStd, bcFeaturesMean)(_)
+ new RDDLossFunction(instances, getAggregatorFunc, regularization,
$(aggregationDepth))
+ case Huber =>
+ val getAggregatorFunc = new HuberAggregator($(fitIntercept),
$(epsilon), bcFeaturesStd)(_)
+ new RDDLossFunction(instances, getAggregatorFunc, regularization,
$(aggregationDepth))
+ }
+
+ val optimizer = $(loss) match {
+ case SquaredError =>
+ if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
+ new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
} else {
- // If `standardization` is false, we still standardize the data
- // to improve the rate of convergence; as a result, we have to
- // perform this reverse standardization by penalizing each
component
- // differently to get effectively the same objective function
when
- // the training dataset is not standardized.
- if (featuresStd(index) != 0.0) effectiveL1RegParam /
featuresStd(index) else 0.0
+ val standardizationParam = $(standardization)
+ def effectiveL1RegFun = (index: Int) => {
+ if (standardizationParam) {
+ effectiveL1RegParam
+ } else {
+ // If `standardization` is false, we still standardize the
data
+ // to improve the rate of convergence; as a result, we have
to
+ // perform this reverse standardization by penalizing each
component
+ // differently to get effectively the same objective
function when
+ // the training dataset is not standardized.
+ if (featuresStd(index) != 0.0) effectiveL1RegParam /
featuresStd(index) else 0.0
+ }
+ }
+ new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10,
effectiveL1RegFun, $(tol))
}
- }
- new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegFun,
$(tol))
+ case Huber =>
+ val dim = if ($(fitIntercept)) numFeatures + 2 else numFeatures + 1
+ val lowerBounds = BDV[Double](Array.fill(dim)(Double.MinValue))
+ // Optimize huber loss in space "\sigma > 0"
+ lowerBounds(dim - 1) = Double.MinPositiveValue
+ val upperBounds = BDV[Double](Array.fill(dim)(Double.MaxValue))
+ new BreezeLBFGSB(lowerBounds, upperBounds, $(maxIter), 10, $(tol))
+ }
+
+ val initialValues = $(loss) match {
+ case SquaredError =>
+ Vectors.zeros(numFeatures)
+ case Huber =>
+ val dim = if ($(fitIntercept)) numFeatures + 2 else numFeatures + 1
+ Vectors.dense(Array.fill(dim)(1.0))
--- End diff --
out of curiosity, is there a reference for the default value?
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