Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/3626#discussion_r21403159
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala ---
@@ -65,6 +65,24 @@ class NaiveBayesModel private[mllib] (
override def predict(testData: Vector): Double = {
labels(brzArgmax(brzPi + brzTheta * testData.toBreeze))
}
+
+ def classProbabilities(testData: RDD[Vector]):
+ RDD[scala.collection.mutable.Map[Double, Double]] = {
+ val bcModel = testData.context.broadcast(this)
+ testData.mapPartitions { iter =>
+ val model = bcModel.value
+ iter.map(model.classProbabilities)
+ }
+ }
+
+ def classProbabilities(testData: Vector):
scala.collection.mutable.Map[Double, Double] = {
--- End diff --
That's fine, but you need not promise a mutable Map in the return
type. You can return it as a scala.collection.Map
On Fri, Dec 5, 2014 at 3:51 PM, alanctgardner <[email protected]>
wrote:
> In
>
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala:
>
>> @@ -65,6 +65,24 @@ class NaiveBayesModel private[mllib] (
>> override def predict(testData: Vector): Double = {
>> labels(brzArgmax(brzPi + brzTheta * testData.toBreeze))
>> }
>> +
>> + def classProbabilities(testData: RDD[Vector]):
>> + RDD[scala.collection.mutable.Map[Double, Double]] = {
>> + val bcModel = testData.context.broadcast(this)
>> + testData.mapPartitions { iter =>
>> + val model = bcModel.value
>> + iter.map(model.classProbabilities)
>> + }
>> + }
>> +
>> + def classProbabilities(testData: Vector):
>> scala.collection.mutable.Map[Double, Double] = {
>
> Scala newbie. I couldn't find a better pattern to build the map than
> mutating it in the foreach. Should I just build a map then make it
immutable
> for returning?
>
> â
> Reply to this email directly or view it on GitHub.
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