Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/2455#discussion_r18124397
--- Diff:
core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala ---
@@ -53,56 +81,237 @@ 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)
extends RandomSampler[T, T] {
- private[random] var rng: Random = new XORShiftRandom
+ // epsilon slop to avoid failure from floating point jitter
+ @transient val eps: Double = RandomSampler.epsArgs
+ require(lb <= (ub + eps), "Lower bound (lb) must be <= upper bound (ub)")
+ require(lb >= (0d - eps), "Lower bound (lb) must be >= 0.0")
+ require(ub <= (1d + eps), "Upper bound (ub) must be <= 1.0")
- def this(ratio: Double) = this(0.0d, ratio)
+ private val rng: Random = new XORShiftRandom
override def setSeed(seed: Long) = rng.setSeed(seed)
override def sample(items: Iterator[T]): Iterator[T] = {
- items.filter { item =>
- val x = rng.nextDouble()
- (x >= lb && x < ub) ^ complement
+ ub-lb match {
+ case f if (f <= 0d) => if (complement) items else Iterator.empty
+ case _ => {
+ if (complement) {
+ items.filter(item => {
+ val x = rng.nextDouble()
+ (x < lb) || (x >= ub)
+ })
+ } else {
+ items.filter(item => {
+ val x = rng.nextDouble()
+ (x >= lb) && (x < ub)
+ })
+ }
+ }
}
}
/**
* Return a sampler that is the complement of the range specified of
the current sampler.
*/
- def cloneComplement(): BernoulliSampler[T] = new BernoulliSampler[T](lb,
ub, !complement)
+ def cloneComplement(): BernoulliPartitionSampler[T] =
+ new BernoulliPartitionSampler[T](lb, ub, !complement)
- override def clone = new BernoulliSampler[T](lb, ub, complement)
+ override def clone = new BernoulliPartitionSampler[T](lb, ub, complement)
}
+
/**
* :: DeveloperApi ::
- * A sampler based on values drawn from Poisson distribution.
+ * A sampler based on Bernoulli trials.
*
- * @param mean Poisson mean
+ * @param fraction the sampling fraction, aka Bernoulli sampling
probability
* @tparam T item type
*/
@DeveloperApi
-class PoissonSampler[T](mean: Double) extends RandomSampler[T, T] {
+class BernoulliSampler[T: ClassTag](fraction: Double) extends
RandomSampler[T, T] {
+
+ // epsilon slop to avoid failure from floating point jitter
+ @transient val eps: Double = RandomSampler.epsArgs
+ require(fraction >= (0.0 - eps) && fraction <= (1.0 + eps),
+ "Sampling fraction must be on interval [0, 1]")
+
+ private val rng: Random = RandomSampler.rngDefault
- private[random] var rng = new Poisson(mean, new DRand)
+ override def setSeed(seed: Long) = rng.setSeed(seed)
+
+ override def sample(items: Iterator[T]): Iterator[T] = {
+ fraction match {
+ case f if (f <= 0.0) => Iterator.empty
+ case f if (f >= 1.0) => items
+ case f if (f <= RandomSampler.gsmDefault) =>
+ new GapSamplingIterator(items, f, rng, RandomSampler.epsDefault)
+ case _ => items.filter(_ => (rng.nextDouble() <= fraction))
+ }
+ }
+
+ override def clone = new BernoulliSampler[T](fraction)
+}
+
+
+/**
+ * :: DeveloperApi ::
+ * A sampler for sampling with replacement, based on values drawn from
Poisson distribution.
+ *
+ * @param fraction the sampling fraction (with replacement)
+ * @tparam T item type
+ */
+@DeveloperApi
+class PoissonSampler[T: ClassTag](fraction: Double) extends
RandomSampler[T, T] {
+
+ // epsilon slop to avoid failure from floating point jitter
+ @transient val eps = RandomSampler.epsArgs
+ require(fraction >= (0.0 - eps), "Sampling fraction must be >= 0")
+
+ private var curseed: Long = System.nanoTime
+ private var rng = new Poisson(fraction, new DRand(curseed.toInt))
override def setSeed(seed: Long) {
- rng = new Poisson(mean, new DRand(seed.toInt))
+ curseed = seed
+ rng = new Poisson(fraction, new DRand(seed.toInt))
}
override def sample(items: Iterator[T]): Iterator[T] = {
- items.flatMap { item =>
- val count = rng.nextInt()
- if (count == 0) {
- Iterator.empty
- } else {
- Iterator.fill(count)(item)
+ fraction match {
+ case f if (f <= 0.0) => Iterator.empty
+ case f if (f <= RandomSampler.gsmDefault) => {
+ val trng = RandomSampler.rngDefault
+ trng.setSeed(curseed)
+ new GapSamplingReplacementIterator(items, f, trng,
RandomSampler.epsDefault)
}
+ case _ => items.flatMap(item => {
+ val count = rng.nextInt()
+ if (count == 0) Iterator.empty else Iterator.fill(count)(item)
+ })
+ }
+ }
+
+ override def clone = new PoissonSampler[T](fraction)
+}
+
+
+@DeveloperApi
+private [spark]
+class GapSamplingIterator[T: ClassTag](var data: Iterator[T], f: Double,
+ rng: Random =
RandomSampler.rngDefault,
+ epsilon: Double =
RandomSampler.epsDefault
+ ) extends Iterator[T] {
+
+ require(f > 0.0 && f < 1.0, "Sampling fraction (f) must reside on open
interval (0, 1)")
+ require(epsilon > 0.0, "epsilon must be > 0")
+
+ // implement efficient linear-sequence drop until scala includes fix for
jira SI-8835
+ private val dd: Int => Unit = {
+ val arrayClass = Array.empty[T].iterator.getClass
--- End diff --
Is this going to be inefficient to keep accessing the class and its name on
every invocation?
And rather than match on class names, can you just match an instance of
`Array` or `ArrayBuffer`? I may be missing a reason you can't do that in Scala
here. I understand you're avoiding calling `drop` for other types.
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