Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7672#discussion_r35929851
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala ---
@@ -129,29 +129,51 @@ class NaiveBayesModel private[ml] (
throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
}
- override protected def predict(features: Vector): Double = {
+ override val numClasses: Int = pi.size
+
+ private def posteriorProbabilities(logProb: DenseVector) = {
+ val logProbArray = logProb.toArray
+ val maxLog = logProbArray.max
+ val scaledProbs = logProbArray.map(lp => math.exp(lp - maxLog))
+ val probSum = scaledProbs.sum
+ new DenseVector(scaledProbs.map(_ / probSum))
+ }
+
+ private def multinomialCalculation(testData: Vector) = {
+ val prob = theta.multiply(testData)
+ BLAS.axpy(1.0, pi, prob)
+ prob
+ }
+
+ private def bernoulliCalculation(testData: Vector) = {
+ testData.foreachActive((_, 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, pi, prob)
+ BLAS.axpy(1.0, negThetaSum.get, prob)
+ prob
+ }
+
+ override protected def predictRaw(features: Vector): Vector = {
$(modelType) match {
case Multinomial =>
- val prob = theta.multiply(features)
- BLAS.axpy(1.0, pi, prob)
- prob.argmax
+ multinomialCalculation(features)
case Bernoulli =>
- features.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 $features")
- }
- }
- val prob = thetaMinusNegTheta.get.multiply(features)
- BLAS.axpy(1.0, pi, prob)
- BLAS.axpy(1.0, negThetaSum.get, prob)
- prob.argmax
+ bernoulliCalculation(features)
case _ =>
// This should never happen.
throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
}
}
+ override protected def raw2probabilityInPlace(rawPrediction: Vector):
Vector = {
+ posteriorProbabilities(rawPrediction.toDense)
--- End diff --
This should operate in place, but it is not currently.
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