Github user falaki commented on a diff in the pull request:
https://github.com/apache/spark/pull/1025#discussion_r14688398
--- Diff:
core/src/main/scala/org/apache/spark/util/random/StratifiedSampler.scala ---
@@ -0,0 +1,310 @@
+/*
+ * 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
+import scala.collection.mutable.{ArrayBuffer, HashMap, Map => MMap}
+
+import org.apache.commons.math3.random.RandomDataGenerator
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+
+/**
+ * Auxiliary functions and data structures for the sampleByKey method in
PairRDDFunctions.
+ *
+ * Essentially, when exact sample size is necessary, we make additional
passes over the RDD to
+ * compute the exact threshold value to use for each stratum to guarantee
exact sample size with
+ * high probability. This is achieved by maintaining a waitlist of size
O(log(s)), where s is the
+ * desired sample size for each stratum.
+ *
+ * Like in simple random sampling, we generate a random value for each
item from the
+ * uniform distribution [0.0, 1.0]. All items with values <= min(values
of items in the waitlist)
+ * are accepted into the sample instantly. The threshold for instant
accept is designed so that
+ * s - numAccepted = O(log(s)), where s is again the desired sample size.
Thus, by maintaining a
+ * waitlist size = O(log(s)), we will be able to create a sample of the
exact size s by adding
+ * a portion of the waitlist to the set of items that are instantly
accepted. The exact threshold
+ * is computed by sorting the values in the waitlist and picking the value
at (s - numAccepted).
+ *
+ * Note that since we use the same seed for the RNG when computing the
thresholds and the actual
+ * sample, our computed thresholds are guaranteed to produce the desired
sample size.
+ *
+ * For more theoretical background on the sampling techniques used here,
please refer to
+ * http://jmlr.org/proceedings/papers/v28/meng13a.html
+ */
+
+private[spark] object StratifiedSampler extends Logging {
+
+ /**
+ * Count the number of items instantly accepted and generate the
waitlist for each stratum.
+ *
+ * This is only invoked when exact sample size is required.
+ */
+ def getCounts[K, V](rdd: RDD[(K, V)],
+ withReplacement: Boolean,
+ fractions: Map[K, Double],
+ counts: Option[Map[K, Long]],
+ seed: Long): MMap[K, Stratum] = {
+ val combOp = getCombOp[K]
+ val mappedPartitionRDD = rdd.mapPartitionsWithIndex({ case (partition,
iter) =>
+ val zeroU: MMap[K, Stratum] = new HashMap[K, Stratum]()
+ val rng = new RandomDataGenerator()
+ rng.reSeed(seed + partition)
+ val seqOp = getSeqOp(withReplacement, fractions, rng, counts)
+ Iterator(iter.aggregate(zeroU)(seqOp, combOp))
+ }, preservesPartitioning=true)
+ mappedPartitionRDD.reduce(combOp)
+ }
+
+ /**
+ * Returns the function used by aggregate to collect sampling statistics
for each partition.
+ */
+ def getSeqOp[K, V](withReplacement: Boolean,
+ fractions: Map[K, Double],
+ rng: RandomDataGenerator,
+ counts: Option[Map[K, Long]]): (MMap[K, Stratum], (K, V)) => MMap[K,
Stratum] = {
+ val delta = 5e-5
+ (result: MMap[K, Stratum], item: (K, V)) => {
+ val key = item._1
+ val fraction = fractions(key)
+ if (!result.contains(key)) {
+ result += (key -> new Stratum())
+ }
+ val stratum = result(key)
+
+ if (withReplacement) {
+ // compute acceptBound and waitListBound 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.areBoundsEmpty) {
+ val n = counts.get(key)
+ val sampleSize = math.ceil(n * fraction).toLong
+ val lmbd1 = PoissonBounds.getLowerBound(sampleSize)
+ val minCount = PoissonBounds.getMinCount(lmbd1)
+ val lmbd2 = if (lmbd1 == 0) {
+ PoissonBounds.getUpperBound(sampleSize)
+ } else {
+ PoissonBounds.getUpperBound(sampleSize - minCount)
+ }
+ stratum.acceptBound = lmbd1 / n
+ stratum.waitListBound = lmbd2 / n
+ }
+ val acceptBound = stratum.acceptBound
+ val copiesAccepted = if (acceptBound == 0.0) 0L else
rng.nextPoisson(acceptBound)
+ if (copiesAccepted > 0) {
+ stratum.incrNumAccepted(copiesAccepted)
+ }
+ val copiesWaitlisted = rng.nextPoisson(stratum.waitListBound).toInt
+ if (copiesWaitlisted > 0) {
+
stratum.addToWaitList(ArrayBuffer.fill(copiesWaitlisted)(rng.nextUniform(0.0,
1.0)))
+ }
+ } else {
+ // We use the streaming version of the algorithm for sampling
without replacement to avoid
+ // using an extra pass over the RDD for computing the count.
+ // Hence, acceptBound and waitListBound change on every iteration.
+ val g1 = - math.log(delta) / stratum.numItems // gamma1
+ val g2 = (2.0 / 3.0) * g1 // gamma 2
+ stratum.acceptBound = math.max(0, fraction + g2 - math.sqrt(g2 *
g2 + 3 * g2 * fraction))
+ stratum.waitListBound = math.min(1, fraction + g1 + math.sqrt(g1 *
g1 + 2 * g1 * fraction))
+
+ val x = rng.nextUniform(0.0, 1.0)
+ if (x < stratum.acceptBound) {
+ stratum.incrNumAccepted()
+ } else if (x < stratum.waitListBound) {
+ stratum.addToWaitList(x)
+ }
+ }
+ stratum.incrNumItems()
+ result
+ }
+ }
+
+ /**
+ * Returns the function used combine results returned by seqOp from
different partitions.
+ */
+ def getCombOp[K]: (MMap[K, Stratum], MMap[K, Stratum]) => MMap[K,
Stratum] = {
+ (r1: MMap[K, Stratum], r2: MMap[K, Stratum]) => {
+ // take union of both key sets in case one partition doesn't contain
all keys
+ for (key <- r1.keySet.union(r2.keySet)) {
+ // Use r2 to keep the combined result since r1 is usual empty
+ val entry1 = r1.get(key)
+ if (r2.contains(key)) {
+ r2(key).merge(entry1)
+ } else {
+ if (entry1.isDefined) {
+ r2 += (key -> entry1.get)
+ }
+ }
+ }
+ r2
+ }
+ }
+
+ /**
+ * Given the result returned by getCounts, determine the threshold for
accepting items to
+ * generate exact sample size.
+ *
+ * To do so, we compute sampleSize = math.ceil(size * samplingRate) for
each stratum and compare
+ * it to the number of items that were accepted instantly and the number
of items in the waitlist
+ * for that stratum. Most of the time, numAccepted <= sampleSize <=
(numAccepted + numWaitlisted),
+ * which means we need to sort the elements in the waitlist by their
associated values in order
+ * to find the value T s.t. |{elements in the stratum whose associated
values <= T}| = sampleSize.
+ * Note that all elements in the waitlist have values >= bound for
instant accept, so a T value
+ * in the waitlist range would allow all elements that were instantly
accepted on the first pass
+ * to be included in the sample.
+ */
+ def computeThresholdByKey[K](finalResult: Map[K, Stratum],
+ fractions: Map[K, Double]): Map[K, Double] = {
+ val thresholdByKey = new HashMap[K, Double]()
+ for ((key, stratum) <- finalResult) {
+ val sampleSize = math.ceil(stratum.numItems * fractions(key)).toLong
+ if (stratum.numAccepted > sampleSize) {
+ logWarning("Pre-accepted too many")
+ thresholdByKey += (key -> stratum.acceptBound)
+ } else {
+ val numWaitListAccepted = (sampleSize - stratum.numAccepted).toInt
+ if (numWaitListAccepted >= stratum.waitList.size) {
+ logWarning("WaitList too short")
+ thresholdByKey += (key -> stratum.waitListBound)
+ } 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)],
+ fractions: Map[K, Double],
+ exact: Boolean,
+ seed: Long): (Int, Iterator[(K, V)]) => Iterator[(K, V)] = {
+ var samplingRateByKey = fractions
+ if (exact) {
+ // determine threshold for each stratum and resample
+ val finalResult = getCounts(rdd, false, fractions, None, seed)
+ samplingRateByKey = computeThresholdByKey(finalResult, fractions)
+ }
+ (idx: Int, iter: Iterator[(K, V)]) => {
+ val rng = new RandomDataGenerator
+ rng.reSeed(seed + idx)
+ iter.filter(t => rng.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)],
+ fractions: Map[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 finalResult = getCounts(rdd, true, fractions, counts, seed)
+ val thresholdByKey = computeThresholdByKey(finalResult, fractions)
+ (idx: Int, iter: Iterator[(K, V)]) => {
+ val rng = new RandomDataGenerator()
+ rng.reSeed(seed + idx)
+ iter.flatMap { item =>
+ val key = item._1
+ val q1 = finalResult(key).acceptBound
+ val q2 = finalResult(key).waitListBound
--- End diff --
Similarly for q1 and q2, more descriptive names can help.
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