Github user erikerlandson commented on a diff in the pull request:
https://github.com/apache/spark/pull/2455#discussion_r18791243
--- Diff:
core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala ---
@@ -53,56 +89,238 @@ trait RandomSampler[T, U] extends Pseudorandom with
Cloneable with Serializable
* @tparam T item type
*/
@DeveloperApi
-class BernoulliSampler[T](lb: Double, ub: Double, complement: Boolean =
false)
+class BernoulliPartitionSampler[T](lb: Double, ub: Double, complement:
Boolean = false)
--- End diff --
My opinion on naming is still that `BernoulliSampler` ought to refer to the
object that does straight Bernoulli sampling (without added data partitioning
semantics). It seems misleading to have an object called `BernoulliSampler`
that introduces additional class parameters and extra computation under the
hood to support a specialized use case. Naming that class
`BernoulliCellSampler` captures the idea that it is the variation with a
specialized purpose.
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