Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/12819#discussion_r81105501
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -355,79 +356,33 @@ class NaiveBayes private (
*/
@Since("0.9.0")
def run(data: RDD[LabeledPoint]): NaiveBayesModel = {
- val requireNonnegativeValues: Vector => Unit = (v: Vector) => {
- val values = v match {
- case sv: SparseVector => sv.values
- case dv: DenseVector => dv.values
- }
- if (!values.forall(_ >= 0.0)) {
- throw new SparkException(s"Naive Bayes requires nonnegative
feature values but found $v.")
- }
- }
+ val spark = SparkSession
+ .builder()
+ .sparkContext(data.context)
+ .getOrCreate()
- val requireZeroOneBernoulliValues: Vector => Unit = (v: Vector) => {
- val values = v match {
- case sv: SparseVector => sv.values
- case dv: DenseVector => dv.values
- }
- if (!values.forall(v => v == 0.0 || v == 1.0)) {
- throw new SparkException(
- s"Bernoulli naive Bayes requires 0 or 1 feature values but found
$v.")
- }
- }
+ import spark.implicits._
- // Aggregates term frequencies per label.
- // TODO: Calling combineByKey and collect creates two stages, we can
implement something
- // TODO: similar to reduceByKeyLocally to save one stage.
- val aggregated = data.map(p => (p.label,
p.features)).combineByKey[(Long, DenseVector)](
- createCombiner = (v: Vector) => {
- if (modelType == Bernoulli) {
- requireZeroOneBernoulliValues(v)
- } else {
- requireNonnegativeValues(v)
- }
- (1L, v.copy.toDense)
- },
- mergeValue = (c: (Long, DenseVector), v: Vector) => {
- requireNonnegativeValues(v)
- BLAS.axpy(1.0, v, c._2)
- (c._1 + 1L, c._2)
- },
- mergeCombiners = (c1: (Long, DenseVector), c2: (Long, DenseVector))
=> {
- BLAS.axpy(1.0, c2._2, c1._2)
- (c1._1 + c2._1, c1._2)
- }
- ).collect().sortBy(_._1)
+ val nb = new NewNaiveBayes()
+ .setModelType(modelType)
+ .setSmoothing(lambda)
- val numLabels = aggregated.length
- var numDocuments = 0L
- aggregated.foreach { case (_, (n, _)) =>
- numDocuments += n
- }
- val numFeatures = aggregated.head match { case (_, (_, v)) => v.size }
-
- val labels = new Array[Double](numLabels)
- val pi = new Array[Double](numLabels)
- val theta = Array.fill(numLabels)(new Array[Double](numFeatures))
-
- val piLogDenom = math.log(numDocuments + numLabels * lambda)
- var i = 0
- aggregated.foreach { case (label, (n, sumTermFreqs)) =>
- labels(i) = label
- pi(i) = math.log(n + lambda) - piLogDenom
- val thetaLogDenom = modelType match {
- case Multinomial => math.log(sumTermFreqs.values.sum + numFeatures
* lambda)
- case Bernoulli => math.log(n + 2.0 * lambda)
- case _ =>
- // This should never happen.
- throw new UnknownError(s"Invalid modelType: $modelType.")
- }
- var j = 0
- while (j < numFeatures) {
- theta(i)(j) = math.log(sumTermFreqs(j) + lambda) - thetaLogDenom
- j += 1
- }
- i += 1
+ val labels = data.map(_.label).distinct().collect().sorted
--- End diff --
It's not necessary to get labels by RDD operation which is expensive. Since
we sort the labels and guarantee it in range [0, numClass), we can use ```val
labels = pi.indices.map(_.toDouble).toArray``` directly.
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