Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3833#discussion_r23821635
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala ---
@@ -81,14 +138,101 @@ class LogisticGradient extends Gradient {
label: Double,
weights: Vector,
cumGradient: Vector): Double = {
- val margin = -1.0 * dot(data, weights)
- val gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label
- axpy(gradientMultiplier, data, cumGradient)
- if (label > 0) {
- // The following is equivalent to log(1 + exp(margin)) but more
numerically stable.
- MLUtils.log1pExp(margin)
- } else {
- MLUtils.log1pExp(margin) - margin
+ assert((weights.size % data.size) == 0)
+ val dataSize = data.size
+
+ // (n + 1) is number of classes
+ val n = weights.size / dataSize
+ n match {
+ case 1 =>
+ /**
+ * For Binary Logistic Regression.
+ *
+ * Although the loss and gradient calculation for multinomial one
is more generalized,
+ * and multinomial one can also be used in binary case, we still
implement a specialized
+ * binary version for performance reason.
+ */
+ val margin = -1.0 * dot(data, weights)
+ val multiplier = (1.0 / (1.0 + math.exp(margin))) - label
+ axpy(multiplier, data, cumGradient)
+ if (label > 0) {
+ // The following is equivalent to log(1 + exp(margin)) but more
numerically stable.
+ MLUtils.log1pExp(margin)
+ } else {
+ MLUtils.log1pExp(margin) - margin
+ }
+ case _ =>
+ /**
+ * For Multinomial Logistic Regression.
+ */
+ val weightsArray = weights match {
+ case dv: DenseVector => dv.values
+ case _ =>
+ throw new IllegalArgumentException(
+ s"weights only supports dense vector but got type
${weights.getClass}.")
+ }
+ val cumGradientArray = cumGradient match {
+ case dv: DenseVector => dv.values
+ case _ =>
+ throw new IllegalArgumentException(
+ s"cumGradient only supports dense vector but got type
${cumGradient.getClass}.")
+ }
+
+ // marginY is margins(label - 1) in the formula.
+ var marginY = 0.0
+ var maxMargin = Double.NegativeInfinity
+ var maxMarginIndex = 0
+
+ val margins = (0 until n).map { i =>
--- End diff --
`Array.tabulate`
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