Github user thunterdb commented on a diff in the pull request:
https://github.com/apache/spark/pull/12819#discussion_r87029278
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala ---
@@ -109,10 +118,89 @@ 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
+ }
+
+ require(values.forall(_ >= 0.0),
+ 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
+ }
+
+ require(values.forall(v => v == 0.0 || v == 1.0),
+ s"Bernoulli naive Bayes requires 0 or 1 feature values but found
$v.")
+ }
+
+ val requireValues: Vector => Unit = {
+ $(modelType) match {
--- End diff --
Looking at the decompiled bytecode, the situation is a bit more
complicated: the pattern match is inlined and is not wrapped in a closure, so
it would work as you would expect. However, because this relies on a compiler
optimization, we should still evaluate the model type before outside
`requireValues`.
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