Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6073#discussion_r30194875
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -293,12 +298,26 @@ class NaiveBayes private (
}
}
+ val requireZeroOneBernoulliValues: Vector => Unit = (v: Vector) => {
+ val values = v match {
+ case SparseVector(size, indices, values) =>
+ values
+ case DenseVector(values) =>
+ 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.")
+ }
+ }
+
// 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, BDV[Double])](
createCombiner = (v: Vector) => {
requireNonnegativeValues(v)
--- End diff --
This does need to be called for the Bernoulli model in the createCombiner.
Can you please make this an if-else?
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