Github user dorx commented on a diff in the pull request:
https://github.com/apache/spark/pull/1025#discussion_r14688633
--- 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
--- End diff --
I was trying to make the next two lines fit on the same lines, but it
doesn't actually look too bad breaking them into two lines each.
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