Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/6761#discussion_r32474655
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -113,6 +106,55 @@ class NaiveBayesModel private[mllib] (
}
}
+ def predictProbabilities(testData: RDD[Vector]): RDD[Map[Double,
Double]] = {
+ val bcModel = testData.context.broadcast(this)
+ testData.mapPartitions { iter =>
+ val model = bcModel.value
+ iter.map(model.predictProbabilities)
+ }
+ }
+
+ def predictProbabilities(testData: Vector): Map[Double, Double] = {
+ modelType match {
+ case Multinomial =>
+ val prob = multinomialCalculation(testData)
+ posteriorProbabilities(prob)
+ case Bernoulli =>
+ val prob = bernoulliCalculation(testData)
+ posteriorProbabilities(prob)
+ case _ =>
+ // This should never happen.
+ throw new UnknownError(s"Invalid modelType: $modelType.")
+ }
+ }
+
+ protected[classification] def multinomialCalculation(testData: Vector):
DenseVector = {
+ val prob = thetaMatrix.multiply(testData)
+ BLAS.axpy(1.0, piVector, prob)
+ prob
+ }
+
+ protected[classification] def bernoulliCalculation(testData: Vector):
DenseVector = {
+ 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.")
+ }
+ }
+ val prob = thetaMinusNegTheta.get.multiply(testData)
+ BLAS.axpy(1.0, piVector, prob)
+ BLAS.axpy(1.0, negThetaSum.get, prob)
+ prob
+ }
+
+ protected[classification] def posteriorProbabilities(prob: DenseVector):
Map[Double, Double] = {
+ val maxLogs = max(prob.toBreeze)
+ val minLogs = min(prob.toBreeze)
+ val normalized = prob.toArray.map(e => (e - minLogs) / (maxLogs -
minLogs))
--- End diff --
This is on the right path except that the 'normalization' upfront here
isn't valid. Just drop that. I meant you have to subtract the sum of the log of
probabilities rather than of log-probabilities, but then I think the easiest
thing is just to go straight back to probabilities, then normalize. This
introduces some issues with very small probabilities, but I assume the API must
return probabilities and not log-probabilities, so this is going to be an issue
no matter what. I think this might be about right:
```
val probabilities = prob.toArray.map(math.exp)
val probSum = probabilities.sum
labels.zip(probabilities.map(_ / probSum)).toMap
```
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