Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r74678564
--- 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
--- End diff --
Based on your documentation on `Feature scaling`, you said that you're
caching the `scaled features` to improve the performance, then why do you need
to divide by `featuresStd(index)` if `standardize == true`?
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