Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/12819#discussion_r81225025
--- 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 --
Yeah, so I think this is going to introduce a performance regression into
the mllib version. Before, you could get the labels from the aggregates, but if
we make mllib call into the ml implementation we need an extra pass through the
data. One way to avoid this is leave the mllib implementation untouched, which
has been done in the past with GBT and RF. It's not a great solution, I'm open
to other suggestions.
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