Github user yanboliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/12819#discussion_r79569671
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
    @@ -355,79 +357,32 @@ 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()
    +      .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
    +
    +    // Input labels for [[org.apache.spark.ml.classification.NaiveBayes]] 
must be
    +    // in range [0, numClasses).
    +    val dataset = data.map {
    +      case LabeledPoint(label, features) =>
    +        (labels.indexOf(label).toDouble, features.asML)
    +    }.toDF("label", "features")
    --- End diff --
    
    It's better to compare the performance between ```toDF``` and 
```createDataFrame```, and the regression performance is also necessary.


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