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From dorx <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-2082] stratified sampling in PairRDDFun...
Date Mon, 14 Jul 2014 22:00:46 GMT
Github user dorx commented on a diff in the pull request:

    https://github.com/apache/spark/pull/1025#discussion_r14907155
  
    --- 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,
    +          fraction + gamma2 - math.sqrt(gamma2 * gamma2 + 3 * gamma2 * fraction))
    +        stratum.waitListBound = math.min(1,
    +          fraction + gamma1 + math.sqrt(gamma1 * gamma1 + 2 * gamma1 * 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] = {
    +    (result1: MMap[K, Stratum], result2: MMap[K, Stratum]) => {
    +      // take union of both key sets in case one partition doesn't contain all keys
    +      for (key <- result1.keySet.union(result2.keySet)) {
    +        // Use result2 to keep the combined result since r1 is usual empty
    --- End diff --
    
    contract per...? I'm not seeing it in the Scala docs: http://www.scala-lang.org/api/2.10.3/index.html#scala.collection.immutable.List



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