Spark Mllib之分层抽样

Spark中组件Mllib的学习之基础概念篇 
1、解释 
分层抽样的概念就不讲了，具体的操作： 
RDD有个操作可以直接进行抽样：sampleByKey和sample等，这里主要介绍这两个 
（1）将字符串长度为2划分为层2，字符串长度为3划分为层1，对层1和层2按不同的概率进行抽样 
数据

aa
bb
cc
dd
ee
aaa
bbb
ccc
ddd
eee

比如： 
val fractions: Map[Int, Double] = List((1, 0.2), (2, 0.8)).toMap //设定抽样格式 
sampleByKey(withReplacement = false, fractions, 0) 
fractions表示在层1抽0.2，在层2中抽0.8 
withReplacement false表示不重复抽样 
0表示随机的seed

源码：

 /**
   * Return a subset of this RDD sampled by key (via stratified sampling).
   *
   * Create a sample of this RDD using variable sampling rates for different keys as specified by
   * `fractions`, a key to sampling rate map, via simple random sampling with one pass over the
   * RDD, to produce a sample of size that's approximately equal to the sum of
   * math.ceil(numItems * samplingRate) over all key values.
   *
   * @param withReplacement whether to sample with or without replacement
   * @param fractions map of specific keys to sampling rates
   * @param seed seed for the random number generator
   * @return RDD containing the sampled subset
   */
  def sampleByKey(withReplacement: Boolean,
      fractions: Map[K, Double],
      seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope {

    require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.")

    val samplingFunc = if (withReplacement) {
      StratifiedSamplingUtils.getPoissonSamplingFunction(self, fractions, false, seed)
    } else {
      StratifiedSamplingUtils.getBernoulliSamplingFunction(self, fractions, false, seed)
    }
    self.mapPartitionsWithIndex(samplingFunc, preservesPartitioning = true)
  }

2、代码：

import org.apache.spark.{SparkConf, SparkContext}

object StratifiedSamplingLearning {
  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster("local[4]").setAppName(this.getClass.getSimpleName.filter(!_.equals('$')))
    val sc = new SparkContext(conf)
    println("First:")
    val data = sc.textFile("D:\\TestData\\StratifiedSampling.txt") //读取数
      .map(row => {
      //开始处理
      if (row.length == ) //判断字符数
        (row, ) //建立对应map
      else (row, ) //建立对应map
    }).map(each => (each._2, each._1))
    data.foreach(println)

    println("sampleByKey:")
    val fractions: Map[Int, Double] = List((, 0.2), (, 0.8)).toMap //设定抽样格式
    val approxSample = data.sampleByKey(withReplacement = false, fractions, ) //计算抽样样本
    approxSample.foreach(println)

    println("Second:")
    val randRDD = sc.parallelize(List((, "cat"), (, "mouse"), (, "cup"), (, "book"), (, "tv"), (, "screen"), (, "heater")))
    val sampleMap = List((, 0.4), (, 0.8)).toMap
    val sample2 = randRDD.sampleByKey(false, sampleMap, ).collect
    sample2.foreach(println)

    println("Third:")
    val a = sc.parallelize( to , )
    val b = a.sample(true, 0.8, )
    val c = a.sample(false, 0.8, )
    println("RDD a : " + a.collect().mkString(" , "))
    println("RDD b : " + b.collect().mkString(" , "))
    println("RDD c : " + c.collect().mkString(" , "))
    sc.stop
  }
}

3、结果：

First:
(,aa)
(,bbb)
(,bb)
(,ccc)
(,cc)
(,ddd)
(,dd)
(,eee)
(,ee)
(,aaa)
sampleByKey:
(,aa)
(,bb)
(,cc)
(,ee)
Second:
(,cat)
(,mouse)
(,book)
(,screen)
(,heater)
Third:
RDD a :  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,
RDD b :  ,  ,  ,  ,  ,  ,  ,
RDD c :  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  ,