ykerzhner commented on a change in pull request #31693:
URL: https://github.com/apache/spark/pull/31693#discussion_r585589350
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
##########
@@ -934,31 +934,56 @@ class LogisticRegression @Since("1.2.0") (
instances: RDD[Instance],
actualBlockSizeInMB: Double,
featuresStd: Array[Double],
+ featuresMean: Array[Double],
numClasses: Int,
- initialCoefWithInterceptMatrix: Matrix,
+ initialCoefWithInterceptArray: Array[Double],
regularization: Option[L2Regularization],
optimizer: FirstOrderMinimizer[BDV[Double], DiffFunction[BDV[Double]]])
= {
val numFeatures = featuresStd.length
val bcFeaturesStd = instances.context.broadcast(featuresStd)
+ val bcFeaturesMean = instances.context.broadcast(featuresMean)
- val standardized = instances.mapPartitions { iter =>
+ val scaled = instances.mapPartitions { iter =>
val inverseStd = bcFeaturesStd.value.map { std => if (std != 0) 1.0 /
std else 0.0 }
val func = StandardScalerModel.getTransformFunc(Array.empty, inverseStd,
false, true)
iter.map { case Instance(label, weight, vec) => Instance(label, weight,
func(vec)) }
}
val maxMemUsage = (actualBlockSizeInMB * 1024L * 1024L).ceil.toLong
- val blocks = InstanceBlock.blokifyWithMaxMemUsage(standardized,
maxMemUsage)
+ val blocks = InstanceBlock.blokifyWithMaxMemUsage(scaled, maxMemUsage)
.persist(StorageLevel.MEMORY_AND_DISK)
.setName(s"training blocks (blockSizeInMB=$actualBlockSizeInMB)")
- val getAggregatorFunc = new BlockLogisticAggregator(numFeatures,
numClasses, $(fitIntercept),
- checkMultinomial(numClasses))(_)
+ val multinomial = checkMultinomial(numClasses)
+ val fitWithMean = !multinomial && $(fitIntercept) &&
+ (!isSet(lowerBoundsOnIntercepts) ||
$(lowerBoundsOnIntercepts)(0).isNegInfinity) &&
+ (!isSet(upperBoundsOnIntercepts) ||
$(upperBoundsOnIntercepts)(0).isPosInfinity)
+
+ val costFun = if (multinomial) {
+ // TODO: create a separate BlockMultinomialLogisticAggregator for
clearness
+ val getAggregatorFunc = new BlockLogisticAggregator(numFeatures,
numClasses,
+ $(fitIntercept), true)(_)
+ new RDDLossFunction(blocks, getAggregatorFunc, regularization,
$(aggregationDepth))
+ } else {
+ val getAggregatorFunc = new
BlockBinaryLogisticAggregator(bcFeaturesStd, bcFeaturesMean,
+ $(fitIntercept), fitWithMean)(_)
+ new RDDLossFunction(blocks, getAggregatorFunc, regularization,
$(aggregationDepth))
+ }
+
+ if (fitWithMean) {
+ var i = 0
+ var adapt = 0.0
+ while (i < numFeatures) {
+ if (featuresStd(i) != 0) {
+ adapt += initialCoefWithInterceptArray(i) * featuresMean(i) /
featuresStd(i)
Review comment:
Oops, I wrote the explanation last night, but didnt submit the review
till this morning.
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