Skip to content

[SPARK-7652][MLlib] Update the implementation of naive Bayes prediction with BLAS #6189

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 5 commits into from
Closed

[SPARK-7652][MLlib] Update the implementation of naive Bayes prediction with BLAS #6189

Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
1b6cdfe
Update the implementation of naive Bayes prediction with BLAS.
viirya May 15, 2015
2f65186
Remove toDense.
viirya May 16, 2015
b5772b4
Fix binary compatibility.
viirya May 16, 2015
ddc48b9
Merge remote-tracking branch 'upstream/master' into naive_bayes_blas_…
viirya May 19, 2015
ab611fd
Remove unnecessary space.
viirya May 19, 2015
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
41 changes: 24 additions & 17 deletions mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,13 +21,11 @@ import java.lang.{Iterable => JIterable}

import scala.collection.JavaConverters._

import breeze.linalg.{Axis, DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
import breeze.numerics.{exp => brzExp, log => brzLog}
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._

import org.apache.spark.{Logging, SparkContext, SparkException}
import org.apache.spark.mllib.linalg.{BLAS, DenseVector, SparseVector, Vector}
import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, SparseVector, Vector, Vectors}
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Should be able to remove the breeze imports now.

import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.{Loader, Saveable}
import org.apache.spark.rdd.RDD
Expand All @@ -50,6 +48,9 @@ class NaiveBayesModel private[mllib] (
val modelType: String)
extends ClassificationModel with Serializable with Saveable {

private val piVector = new DenseVector(pi)
private val thetaMatrix = new DenseMatrix(labels.size, theta(0).size, theta.flatten, true)

private[mllib] def this(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) =
this(labels, pi, theta, "Multinomial")

Expand All @@ -60,17 +61,18 @@ class NaiveBayesModel private[mllib] (
theta: JIterable[JIterable[Double]]) =
this(labels.asScala.toArray, pi.asScala.toArray, theta.asScala.toArray.map(_.asScala.toArray))

private val brzPi = new BDV[Double](pi)
private val brzTheta = new BDM(theta(0).length, theta.length, theta.flatten).t

// Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0.
// This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra
// This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra
// application of this condition (in predict function).
private val (brzNegTheta, brzNegThetaSum) = modelType match {
private val (thetaMinusNegTheta, negThetaSum) = modelType match {
case "Multinomial" => (None, None)
case "Bernoulli" =>
val negTheta = brzLog((brzExp(brzTheta.copy) :*= (-1.0)) :+= 1.0) // log(1.0 - exp(x))
(Option(negTheta), Option(brzSum(negTheta, Axis._1)))
val negTheta = thetaMatrix.map(value => math.log(1.0 - math.exp(value)))
val ones = new DenseVector(Array.fill(thetaMatrix.numCols){1.0})
val thetaMinusNegTheta = thetaMatrix.map { value =>
value - math.log(1.0 - math.exp(value))
}
(Option(thetaMinusNegTheta), Option(negTheta.multiply(ones)))
case _ =>
// This should never happen.
throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType")
Expand All @@ -85,17 +87,22 @@ 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)
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)
BLAS.axpy(1.0, piVector, prob)
BLAS.axpy(1.0, negThetaSum.get, prob)
labels(prob.argmax)
case _ =>
// This should never happen.
throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType")
Expand Down