Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r74886863
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -952,13 +963,160 @@ private class LogisticAggregator(
val bcFeaturesStd: Broadcast[Array[Double]],
private val numFeatures: Int,
numClasses: Int,
- fitIntercept: Boolean) extends Serializable {
+ fitIntercept: Boolean,
+ multinomial: Boolean) extends Serializable with Logging {
+
+ private val numFeaturesPlusIntercept = if (fitIntercept) numFeatures + 1
else numFeatures
+ private val coefficientSize = bcCoefficients.value.size
+ if (multinomial) {
+ require(numClasses == coefficientSize / numFeaturesPlusIntercept,
s"The number of " +
+ s"coefficients should be ${numClasses * numFeaturesPlusIntercept}
but was $coefficientSize")
+ } else {
+ require(coefficientSize == numFeaturesPlusIntercept, s"Expected
$numFeaturesPlusIntercept " +
+ s"coefficients but got $coefficientSize")
+ require(numClasses <= 2, s"Binary logistic aggregator requires
numClasses in {1, 2}" +
+ s" but found $numClasses.")
+ }
private var weightSum = 0.0
private var lossSum = 0.0
- private val gradientSumArray =
- Array.ofDim[Double](if (fitIntercept) numFeatures + 1 else numFeatures)
+ private val totalCoefficientLength = {
+ val cols = if (fitIntercept) numFeatures + 1 else numFeatures
+ val rows = if (multinomial) numClasses else 1
+ rows * cols
+ }
+
+ private val gradientSumArray =
Array.ofDim[Double](totalCoefficientLength)
+
+ if (multinomial && numClasses < 2) {
+ logInfo(s"Multinomial logistic regression for binary classification
yields separate " +
+ s"coefficients for positive and negative classes. When no
regularization is applied, the" +
+ s"result will be effectively the same as binary logistic regression.
When regularization" +
+ s"is applied, multinomial loss will produce a result different from
binary loss.")
+ }
+
+ /** Update gradient and loss using binary loss function. */
+ private def binaryUpdateInPlace(
+ features: Vector,
+ weight: Double,
+ label: Double,
+ coefficients: Array[Double],
+ gradient: Array[Double],
+ featuresStd: Array[Double],
+ numFeaturesPlusIntercept: Int): Unit = {
+ val margin = - {
+ var sum = 0.0
+ features.foreachActive { (index, value) =>
+ if (featuresStd(index) != 0.0 && value != 0.0) {
+ sum += coefficients(index) * value / featuresStd(index)
+ }
+ }
+ sum + {
+ if (fitIntercept) coefficients(numFeaturesPlusIntercept - 1) else
0.0
+ }
--- End diff --
For slight clarity,
```scala
if (fitIntercept) {
sum += coefficients(numFeaturesPlusIntercept - 1)
}
sum
```
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