Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6189#discussion_r30432509
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -85,17 +89,22 @@ class NaiveBayesModel private[mllib] (
}
override def predict(testData: Vector): Double = {
- val brzData = testData.toBreeze
modelType match {
case "Multinomial" =>
- labels(brzArgmax(brzPi + brzTheta * brzData))
+ val prob = thetaMatrix.multiply(testData.toDense)
+ BLAS.axpy(1.0, piVector, prob)
+ labels(prob.argmax)
case "Bernoulli" =>
- if (!brzData.forall(v => v == 0.0 || v == 1.0)) {
- throw new SparkException(
- s"Bernoulli Naive Bayes requires 0 or 1 feature values but
found $testData.")
+ testData.foreachActive { (index, value) =>
+ if (value != 0.0 && value != 1.0) {
+ throw new SparkException(
+ s"Bernoulli Naive Bayes requires 0 or 1 feature values but
found $testData.")
+ }
}
- labels(brzArgmax(brzPi +
- (brzTheta - brzNegTheta.get) * brzData + brzNegThetaSum.get))
+ val prob = thetaMinusnegTheta.get.multiply(testData.toDense)
--- End diff --
`toDense` could be quite expensive. It would be great if we first add
`multiply(x: Vector)` and optimize the implementation for `SparseVector`, then
update this PR to use it.
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