Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r74831972
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -945,13 +955,139 @@ class BinaryLogisticRegressionSummary
private[classification] (
private class LogisticAggregator(
private val numFeatures: Int,
numClasses: Int,
- fitIntercept: Boolean) extends Serializable {
+ fitIntercept: Boolean,
+ multinomial: Boolean,
+ standardize: Boolean) extends Serializable {
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)
+
+ /** 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,
+ standardize: Boolean): Unit = {
+ val margin = - {
+ var sum = 0.0
+ features.foreachActive { (index, value) =>
+ if (featuresStd(index) != 0.0 && value != 0.0) {
+ val x = if (standardize) value / featuresStd(index) else value
+ sum += coefficients(index) * x
+ }
+ }
+ sum + {
+ if (fitIntercept) coefficients(numFeaturesPlusIntercept - 1) else
0.0
+ }
+ }
+
+ val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - label)
+
+ features.foreachActive { (index, value) =>
+ if (featuresStd(index) != 0.0 && value != 0.0) {
+ val x = if (standardize) value / featuresStd(index) else value
+ gradient(index) += multiplier * x
+ }
+ }
+
+ if (fitIntercept) {
+ gradient(numFeaturesPlusIntercept - 1) += multiplier
+ }
+
+ if (label > 0) {
+ // The following is equivalent to log(1 + exp(margin)) but more
numerically stable.
+ lossSum += weight * MLUtils.log1pExp(margin)
+ } else {
+ lossSum += weight * (MLUtils.log1pExp(margin) - margin)
+ }
+ }
+
+ /** Update gradient and loss using multinomial loss function. */
+ private def multinomialUpdateInPlace(
--- End diff --
I'll add it to the doc, though the derivation will be different since we
are not using a pivot class.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
