Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/13796#discussion_r74889972
--- 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
+ }
+ }
+
+ val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - label)
+
+ features.foreachActive { (index, value) =>
+ if (featuresStd(index) != 0.0 && value != 0.0) {
+ gradient(index) += multiplier * value / featuresStd(index)
+ }
+ }
+
+ 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 (softmax) loss function.
*/
+ private def multinomialUpdateInPlace(
+ features: Vector,
+ weight: Double,
+ label: Double,
+ coefficients: Array[Double],
+ gradient: Array[Double],
+ featuresStd: Array[Double],
+ numFeaturesPlusIntercept: Int): Unit = {
+ // TODO: use level 2 BLAS operations
+ /*
+ Note: this can still be used when numClasses = 2 for binary
+ logistic regression without pivoting.
+ */
+
+ // marginOfLabel is margins(label) in the formula
+ var marginOfLabel = 0.0
+ var maxMargin = Double.NegativeInfinity
+
+ val margins = Array.tabulate(numClasses) { i =>
+ var margin = 0.0
+ features.foreachActive { (index, value) =>
+ if (featuresStd(index) != 0.0 && value != 0.0) {
+ margin += coefficients(i * numFeaturesPlusIntercept + index) *
value / featuresStd(index)
+ }
+ }
+
+ if (fitIntercept) {
+ margin += coefficients(i * numFeaturesPlusIntercept +
features.size)
+ }
+ if (i == label.toInt) marginOfLabel = margin
+ if (margin > maxMargin) {
+ maxMargin = margin
+ }
+ margin
+ }
+
+ /**
+ * When maxMargin > 0, the original formula could cause overflow.
+ * We address this by subtracting maxMargin from all the margins, so
it's guaranteed
+ * that all of the new margins will be smaller than zero to prevent
arithmetic overflow.
+ */
+ val sum = {
+ var temp = 0.0
+ if (maxMargin > 0) {
+ for (i <- 0 until numClasses) {
+ margins(i) -= maxMargin
+ temp += math.exp(margins(i))
+ }
+ } else {
+ for (i <- 0 until numClasses) {
+ temp += math.exp(margins(i))
+ }
+ }
+ temp
+ }
+
+ for (i <- 0 until numClasses) {
+ val multiplier = math.exp(margins(i)) / sum - {
+ if (label == i) 1.0 else 0.0
+ }
+ features.foreachActive { (index, value) =>
--- End diff --
Consider to move `for (i <- 0 until numClasses)` into the `if statement`
inside `foreachActive` with `while loop`.
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