Github user falaki commented on a diff in the pull request:
https://github.com/apache/spark/pull/1025#discussion_r14544795
--- Diff:
core/src/main/scala/org/apache/spark/util/random/StratifiedSampler.scala ---
@@ -0,0 +1,296 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.random
+
+import scala.collection.{Map, mutable}
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+import org.apache.commons.math3.random.RandomDataGenerator
+import org.apache.spark.{Logging, SparkContext, TaskContext}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
+import org.apache.spark.util.random.{PoissonBounds => PB}
+
+private[spark] object StratifiedSampler extends Logging {
+
+ /**
+ * A version of {@link #aggregate()} that passes the TaskContext to the
function that does
+ * aggregation for each partition. This function avoids creating an
extra depth in the RDD
+ * lineage, as opposed to using mapPartitionsWithId, which results in
slightly improved run time.
+ */
+ def aggregateWithContext[U: ClassTag, T: ClassTag](zeroValue: U)
+ (rdd: RDD[T],
+ seqOp: ((TaskContext, U), T) => U,
+ combOp: (U, U) => U): U = {
+ val sc: SparkContext = rdd.sparkContext
+ // Clone the zero value since we will also be serializing it as part
of tasks
+ var jobResult = Utils.clone(zeroValue,
sc.env.closureSerializer.newInstance())
+ // pad seqOp and combOp with taskContext to conform to aggregate's
signature in TraversableOnce
+ val paddedSeqOp = (arg1: (TaskContext, U), item: T) => (arg1._1,
seqOp(arg1, item))
+ val paddedcombOp = (arg1: (TaskContext, U), arg2: (TaskContext, U)) =>
+ (arg1._1, combOp(arg1._2, arg1._2))
+ val cleanSeqOp = sc.clean(paddedSeqOp)
+ val cleanCombOp = sc.clean(paddedcombOp)
+ val aggregatePartition = (tc: TaskContext, it: Iterator[T]) =>
+ (it.aggregate(tc, zeroValue)(cleanSeqOp, cleanCombOp))._2
+ val mergeResult = (index: Int, taskResult: U) => jobResult =
combOp(jobResult, taskResult)
+ sc.runJob(rdd, aggregatePartition, mergeResult)
+ jobResult
+ }
+
+ /**
+ * Returns the function used by aggregate to collect sampling statistics
for each partition.
+ */
+ def getSeqOp[K, V](withReplacement: Boolean,
+ fractionByKey: (K => Double),
+ counts: Option[Map[K, Long]]): ((TaskContext, Result[K]),(K, V)) =>
Result[K] = {
+ val delta = 5e-5
+ (U: (TaskContext, Result[K]), item: (K, V)) => {
+ val result = U._2
+ val tc = U._1
+ val rng = result.getRand(tc.partitionId)
+ val fraction = fractionByKey(item._1)
+ val stratum = result.getEntry(item._1)
+ if (withReplacement) {
+ // compute q1 and q2 only if they haven't been computed already
+ // since they don't change from iteration to iteration.
+ // TODO change this to the streaming version
+ if (stratum.q1.isEmpty || stratum.q2.isEmpty) {
+ val n = counts.get(item._1)
+ val s = math.ceil(n * fraction).toLong
+ val lmbd1 = PB.getLowerBound(s)
+ val minCount = PB.getMinCount(lmbd1)
+ val lmbd2 = if (lmbd1 == 0) PB.getUpperBound(s) else
PB.getUpperBound(s - minCount)
+ val q1 = lmbd1 / n
+ val q2 = lmbd2 / n
+ stratum.q1 = Some(q1)
+ stratum.q2 = Some(q2)
+ }
+ val x1 = if (stratum.q1.get == 0) 0L else
rng.nextPoisson(stratum.q1.get)
+ if (x1 > 0) {
+ stratum.incrNumAccepted(x1)
+ }
+ val x2 = rng.nextPoisson(stratum.q2.get).toInt
+ if (x2 > 0) {
+ stratum.addToWaitList(ArrayBuffer.fill(x2)(rng.nextUniform(0.0,
1.0)))
+ }
+ } else {
+ // We use the streaming version of the algorithm for sampling
without replacement.
+ // Hence, q1 and q2 change on every iteration.
+ val g1 = - math.log(delta) / stratum.numItems
+ val g2 = (2.0 / 3.0) * g1
+ val q1 = math.max(0, fraction + g2 - math.sqrt((g2 * g2 + 3 * g2 *
fraction)))
+ val q2 = math.min(1, fraction + g1 + math.sqrt(g1 * g1 + 2 * g1 *
fraction))
+
+ val x = rng.nextUniform(0.0, 1.0)
+ if (x < q1) {
+ stratum.incrNumAccepted()
+ } else if (x < q2) {
+ stratum.addToWaitList(x)
+ }
+ stratum.q1 = Some(q1)
+ stratum.q2 = Some(q2)
+ }
+ stratum.incrNumItems()
+ result
+ }
+ }
+
+ /**
+ * Returns the function used by aggregate to combine results from
different partitions, as
+ * returned by seqOp.
+ */
+ def getCombOp[K](): (Result[K], Result[K]) => Result[K] = {
+ (r1: Result[K], r2: Result[K]) => {
+ // take union of both key sets in case one partition doesn't contain
all keys
+ val keyUnion = r1.resultMap.keys.toSet.union(r2.resultMap.keys.toSet)
+
+ // Use r2 to keep the combined result since r1 is usual empty
+ for (key <- keyUnion) {
+ val entry1 = r1.resultMap.get(key)
+ val entry2 = r2.resultMap.get(key)
+ if (entry2.isEmpty && entry1.isDefined) {
+ r2.resultMap += (key -> entry1.get)
+ } else if (entry1.isDefined && entry2.isDefined) {
+ entry2.get.addToWaitList(entry1.get.waitList)
+ entry2.get.incrNumAccepted(entry1.get.numAccepted)
+ entry2.get.incrNumItems(entry1.get.numItems)
+ }
+ }
+ r2
+ }
+ }
+
+ /**
+ * Given the result returned by the aggregate function, we need to
determine the threshold used
+ * to accept items to generate the exact sample size.
+ */
+ def computeThresholdByKey[K](finalResult: Map[K, Stratum],
fractionByKey: (K => Double)):
+ (K => Double) = {
+ val thresholdByKey = new mutable.HashMap[K, Double]()
+ for ((key, stratum) <- finalResult) {
+ val fraction = fractionByKey(key)
+ val s = math.ceil(stratum.numItems * fraction).toLong
+ if (stratum.numAccepted > s) {
+ logWarning("Pre-accepted too many")
+ thresholdByKey += (key -> stratum.q1.get)
+ } else {
+ val numWaitListAccepted = (s - stratum.numAccepted).toInt
+ if (numWaitListAccepted >= stratum.waitList.size) {
+ logWarning("WaitList too short")
+ thresholdByKey += (key -> stratum.q2.get)
+ } else {
+ thresholdByKey += (key ->
stratum.waitList.sorted.apply(numWaitListAccepted))
+ }
+ }
+ }
+ thresholdByKey
+ }
+
+ /**
+ * Return the per partition sampling function used for sampling without
replacement.
+ *
+ * When exact sample size is required, we make an additional pass over
the RDD to determine the
+ * exact sampling rate that guarantees sample size with high confidence.
+ *
+ * The sampling function has a unique seed per partition.
+ */
+ def getBernoulliSamplingFunction[K, V](rdd: RDD[(K, V)],
+ fractionByKey: K => Double,
+ exact: Boolean,
+ seed: Long): (Int, Iterator[(K, V)]) => Iterator[(K, V)] = {
+ var samplingRateByKey = fractionByKey
+ if (exact) {
+ // determine threshold for each stratum and resample
+ val seqOp = StratifiedSampler.getSeqOp[K,V](false, fractionByKey,
None)
+ val combOp = StratifiedSampler.getCombOp[K]()
+ val zeroU = new Result[K](Map[K, Stratum](), seed = seed)
+ val finalResult = aggregateWithContext(zeroU)(rdd, seqOp,
combOp).resultMap
+ samplingRateByKey =
StratifiedSampler.computeThresholdByKey(finalResult, fractionByKey)
+ }
+ (idx: Int, iter: Iterator[(K, V)]) => {
+ val random = new RandomDataGenerator
+ random.reSeed(seed + idx)
+ iter.filter(t => random.nextUniform(0.0, 1.0) <
samplingRateByKey(t._1))
+ }
+ }
+
+ /**
+ * Return the per partition sampling function used for sampling with
replacement.
+ *
+ * When exact sample size is required, we make two additional passed
over the RDD to determine
+ * the exact sampling rate that guarantees sample size with high
confidence. The first pass
+ * counts the number of items in each stratum (group of items with the
same key) in the RDD, and
+ * the second pass uses the counts to determine exact sampling rates.
+ *
+ * The sampling function has a unique seed per partition.
+ */
+ def getPoissonSamplingFunction[K, V](rdd:RDD[(K, V)],
+ fractionByKey: K => Double,
+ exact: Boolean,
+ counts: Option[Map[K, Long]],
+ seed: Long): (Int, Iterator[(K, V)]) => Iterator[(K, V)] = {
+ // TODO implement the streaming version of sampling w/ replacement
that doesn't require counts
+ if (exact) {
+ val seqOp = StratifiedSampler.getSeqOp[K,V](true, fractionByKey,
counts)
--- End diff --
Similarly remove ```StratifiedSampler``` object name from these lines
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