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From falaki <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-2082] stratified sampling in PairRDDFun...
Date Mon, 07 Jul 2014 21:11:12 GMT
Github user falaki commented on a diff in the pull request:

    https://github.com/apache/spark/pull/1025#discussion_r14622781
  
    --- Diff: core/src/main/scala/org/apache/spark/util/random/StratifiedSampler.scala ---
    @@ -0,0 +1,335 @@
    +/*
    + * 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 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
    +
    +/**
    + * Auxiliary functions and data structures for the sampleByKey method in PairRDDFunctions.
    + *
    + * For more theoretical background on the sampling technqiues used here, please refer
to
    + * http://jmlr.org/proceedings/papers/v28/meng13a.html
    + */
    +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 mapPartitionsWithIndex, 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,
    +      fractions: Map[K, Double],
    +      counts: Option[Map[K, Long]]): ((TaskContext, Result[K]), (K, V)) => Result[K]
= {
    +    val delta = 5e-5
    +    (output: (TaskContext, Result[K]), item: (K, V)) => {
    +      val result = output._2
    +      val tc = output._1
    +      val rng = result.getRand(tc.partitionId)
    +      val fraction = fractions(item._1)
    +      val stratum = result.getEntry(item._1)
    +      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(item._1)
    +          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 x1 = if (stratum.acceptBound == 0.0) 0L else rng.nextPoisson(stratum.acceptBound)
    +        if (x1 > 0) {
    +          stratum.incrNumAccepted(x1)
    +        }
    +        val x2 = rng.nextPoisson(stratum.waitListBound).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
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 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.keySet.union(r2.resultMap.keySet)
    +
    +      // Use r2 to keep the combined result since r1 is usual empty
    +      for (key <- keyUnion) {
    +        val entry1 = r1.resultMap.get(key)
    +        if (r2.resultMap.contains(key)) {
    +          r2.resultMap(key).merge(entry1)
    +        } else {
    +          r2.addEntry(key, entry1)
    +        }
    +      }
    +      r2
    +    }
    +  }
    +
    +  /**
    +   * Given the result returned by the aggregate function, 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]):
    --- End diff --
    
    Style nit:  Return type of the function is in a new line. We can join these two lines.


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