Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/1025#discussion_r14694281 --- Diff: core/src/main/scala/org/apache/spark/util/random/StratifiedSampler.scala --- @@ -0,0 +1,311 @@ +/* + * 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 gamma1 = - math.log(delta) / stratum.numItems + val gamma2 = (2.0 / 3.0) * gamma1 + stratum.acceptBound = math.max(0, --- End diff -- Those bounds are also used in `takeSample`. Should we have BernoulliBounds in SamplingUtils, similar to PoissonBounds?
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