Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3833#discussion_r23821629
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
---
@@ -61,20 +79,58 @@ class LogisticRegressionModel (
override protected def predictPoint(dataMatrix: Vector, weightMatrix:
Vector,
intercept: Double) = {
- val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
- val score = 1.0 / (1.0 + math.exp(-margin))
- threshold match {
- case Some(t) => if (score > t) 1.0 else 0.0
- case None => score
+ // If dataMatrix and weightMatrix have the same dimension, it's binary
logistic regression.
+ if (dataMatrix.size == weightMatrix.size) {
+ val margin = dot(weights, dataMatrix) + intercept
+ val score = 1.0 / (1.0 + math.exp(-margin))
+ threshold match {
+ case Some(t) => if (score > t) 1.0 else 0.0
+ case None => score
+ }
+ } else {
+ val dataWithBiasSize = weightMatrix.size / (nClasses - 1)
+ val dataWithBias = if (dataWithBiasSize == dataMatrix.size) {
+ dataMatrix
+ } else {
+ assert(dataMatrix.size + 1 == dataWithBiasSize)
+ MLUtils.appendBias(dataMatrix)
+ }
+
+ 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 margins = (0 until nClasses - 1).map { i =>
+ var margin = 0.0
+ dataWithBias.foreachActive { (index, value) =>
+ if (value != 0.0) margin += value * weightsArray((i *
dataWithBiasSize) + index)
+ }
+ margin
+ }
+
+ /**
+ * Find the one with maximum margins. If the maxMargin is negative,
then the prediction
+ * result will be the first class.
+ *
+ * PS, if you want to compute the probabilities for each outcome
instead of the outcome
+ * with maximum probability, remember to subtract the maxMargin from
margins if maxMargin
+ * is positive to prevent overflow.
+ */
+ val maxMargin = margins.max
+ if (maxMargin > 0) (margins.indexOf(maxMargin) + 1).toDouble else 0.0
--- End diff --
~~~
var bestClass = 0
var largestMargin = 0.0
var i = 0
while (i < numClasses - 1) {
...
}
bestClass
~~~
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