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

    https://github.com/apache/spark/pull/12819#discussion_r79566824
  
    --- 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)) =>
    +           requireValues(features)
    +           BLAS.axpy(weight, features, agg._2)
    +           (agg._1 + weight, agg._2)
    +      },
    +      combOp = {
    +         case (agg1, agg2) =>
    +           BLAS.axpy(1.0, agg2._2, agg1._2)
    +           (agg1._1 + agg2._1, agg1._2)
    +      }).collect().sortBy(_._1)
    +
    +    val numLabels = aggregated.length
    +    val numDocuments = aggregated.map(_._2._1).sum
    +
    +    val pi = Array.fill[Double](numLabels)(0.0)
    +    val theta = Array.fill[Double](numLabels, numFeatures)(0.0)
    +
    +    val lambda = $(smoothing)
    +    val piLogDenom = math.log(numDocuments + numLabels * lambda)
    +    var i = 0
    +    aggregated.foreach { case (label, (n, sumTermFreqs)) =>
    +      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 uid = Identifiable.randomUID("nb")
    --- End diff --
    
    ```uid``` has been generated at ```def this() = 
this(Identifiable.randomUID("nb"))```, you should use it directly rather than 
generate a new one.


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