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

    https://github.com/apache/spark/pull/12819#discussion_r79603503
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala ---
    @@ -109,10 +119,88 @@ class NaiveBayes @Since("1.5.0") (
             s" numClasses=$numClasses, but thresholds has length 
${$(thresholds).length}")
         }
     
    -    val oldDataset: RDD[OldLabeledPoint] =
    -      extractLabeledPoints(dataset).map(OldLabeledPoint.fromML)
    -    val oldModel = OldNaiveBayes.train(oldDataset, $(smoothing), 
$(modelType))
    -    NaiveBayesModel.fromOld(oldModel, this)
    +    val numFeatures = 
dataset.select(col($(featuresCol))).head().getAs[Vector](0).size
    +
    +    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 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.")
    +      }
    +    }
    +
    +    val requireValues: Vector => Unit = {
    +      $(modelType) match {
    +        case Multinomial =>
    +          requireNonnegativeValues
    +        case Bernoulli =>
    +          requireZeroOneBernoulliValues
    +        case _ =>
    +          // This should never happen.
    +          throw new UnknownError(s"Invalid modelType: ${$(modelType)}.")
    +      }
    +    }
    +
    +    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) 
else col($(weightCol))
    +
    +    val aggregated = dataset.select(col($(labelCol)).cast(DoubleType), w, 
col($(featuresCol))).rdd
    +      .map { row => (row.getDouble(0), (row.getDouble(1), 
row.getAs[Vector](2)))
    +      }.aggregateByKey[(Double, DenseVector)]((0.0, 
Vectors.zeros(numFeatures).toDense))(
    +      seqOp = {
    +         case (agg, (weight, features)) =>
    --- End diff --
    
    ok.


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