Spark机器学习9· 实时机器学习(scala with sbt)-白红宇

1 在线学习

模型随着接收的新消息，不断更新自己；而不是像离线训练一次次重新训练。

2 Spark Streaming

离散化流(DStream)

输入源：Akka actors、消息队列、Flume、Kafka、……

类群(lineage)：应用到RDD上的转换算子和执行算子的集合

3 MLib+Streaming应用

3.0 build.sbt

依赖Spark MLlib和Spark Streaming

name := "scala-spark-streaming-app"version := "1.0"scalaVersion := "2.11.7"libraryDependencies += "org.apache.spark" %% "spark-mllib" % "1.5.1"libraryDependencies += "org.apache.spark" %% "spark-streaming" % "1.5.1"

使用国内镜像仓库

~/.sbt/repositories

[repositories]localosc: http://maven.oschina.net/content/groups/public/typesafe: http://repo.typesafe.com/typesafe/ivy-releases/, [organization]/[module]/(scala_[scalaVersion]/)(sbt_[sbtVersion]/)[revision]/[type]s/[artifact](-[classifier]).[ext], bootOnlysonatype-oss-releasesmaven-centralsonatype-oss-snapshots

3.1 生产消息

object StreamingProducer {  def main(args: Array[String]) {    val random = new Random()    // Maximum number of events per second    val MaxEvents = 6    // Read the list of possible names    val namesResource = this.getClass.getResourceAsStream("/names.csv")    val names = scala.io.Source.fromInputStream(namesResource)      .getLines()      .toList      .head      .split(",")      .toSeq    // Generate a sequence of possible products    val products = Seq(      "iPhone Cover" -> 9.99,      "Headphones" -> 5.49,      "Samsung Galaxy Cover" -> 8.95,      "iPad Cover" -> 7.49    )    /** Generate a number of random product events */    def generateProductEvents(n: Int) = {      (1 to n).map { i =>        val (product, price) = products(random.nextInt(products.size))        val user = random.shuffle(names).head        (user, product, price)      }    }    // create a network producer    val listener = new ServerSocket(9999)    println("Listening on port: 9999")    while (true) {      val socket = listener.accept()      new Thread() {        override def run = {          println("Got client connected from: " + socket.getInetAddress)          val out = new PrintWriter(socket.getOutputStream(), true)          while (true) {            Thread.sleep(1000)            val num = random.nextInt(MaxEvents)            val productEvents = generateProductEvents(num)            productEvents.foreach{ event =>              out.write(event.productIterator.mkString(","))              out.write("\n")            }            out.flush()            println(s"Created $num events...")          }          socket.close()        }      }.start()    }  }}

sbt runMultiple main classes detected, select one to run: [1] MonitoringStreamingModel [2] SimpleStreamingApp [3] SimpleStreamingModel [4] StreamingAnalyticsApp [5] StreamingModelProducer [6] StreamingProducer [7] StreamingStateAppEnter number: 6

3.2 打印消息

object SimpleStreamingApp {  def main(args: Array[String]) {    val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))    val stream = ssc.socketTextStream("localhost", 9999)    // here we simply print out the first few elements of each batch    stream.print()    ssc.start()    ssc.awaitTermination()  }}

sbt runEnter number: 2

3.3 流式分析

object StreamingAnalyticsApp {  def main(args: Array[String]) {    val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))    val stream = ssc.socketTextStream("localhost", 9999)    // create stream of events from raw text elements    val events = stream.map { record =>      val event = record.split(",")      (event(0), event(1), event(2))    }    /*      We compute and print out stats for each batch.      Since each batch is an RDD, we call forEeachRDD on the DStream, and apply the usual RDD functions      we used in Chapter 1.     */    events.foreachRDD { (rdd, time) =>      val numPurchases = rdd.count()      val uniqueUsers = rdd.map { case (user, _, _) => user }.distinct().count()      val totalRevenue = rdd.map { case (_, _, price) => price.toDouble }.sum()      val productsByPopularity = rdd        .map { case (user, product, price) => (product, 1) }        .reduceByKey(_ + _)        .collect()        .sortBy(-_._2)      val mostPopular = productsByPopularity(0)      val formatter = new SimpleDateFormat      val dateStr = formatter.format(new Date(time.milliseconds))      println(s"== Batch start time: $dateStr ==")      println("Total purchases: " + numPurchases)      println("Unique users: " + uniqueUsers)      println("Total revenue: " + totalRevenue)      println("Most popular product: %s with %d purchases".format(mostPopular._1, mostPopular._2))    }    // start the context    ssc.start()    ssc.awaitTermination()  }}

sbt runEnter number: 4

3.4 有状态的流计算

object StreamingStateApp {  import org.apache.spark.streaming.StreamingContext._  def updateState(prices: Seq[(String, Double)], currentTotal: Option[(Int, Double)]) = {    val currentRevenue = prices.map(_._2).sum    val currentNumberPurchases = prices.size    val state = currentTotal.getOrElse((0, 0.0))    Some((currentNumberPurchases + state._1, currentRevenue + state._2))  }  def main(args: Array[String]) {    val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))    // for stateful operations, we need to set a checkpoint location    ssc.checkpoint("/tmp/sparkstreaming/")    val stream = ssc.socketTextStream("localhost", 9999)    // create stream of events from raw text elements    val events = stream.map { record =>      val event = record.split(",")      (event(0), event(1), event(2).toDouble)    }    val users = events.map { case (user, product, price) => (user, (product, price)) }    val revenuePerUser = users.updateStateByKey(updateState)    revenuePerUser.print()    // start the context    ssc.start()    ssc.awaitTermination()  }}

sbt runEnter number: 7

4 线性流回归

线性回归StreamingLinearRegressionWithSGD

trainOn

predictOn

4.1 流数据生成器

object StreamingModelProducer {  import breeze.linalg._  def main(args: Array[String]) {    // Maximum number of events per second    val MaxEvents = 100    val NumFeatures = 100    val random = new Random()    /** Function to generate a normally distributed dense vector */    def generateRandomArray(n: Int) = Array.tabulate(n)(_ => random.nextGaussian())    // Generate a fixed random model weight vector    val w = new DenseVector(generateRandomArray(NumFeatures))    val intercept = random.nextGaussian() * 10    /** Generate a number of random product events */    def generateNoisyData(n: Int) = {      (1 to n).map { i =>        val x = new DenseVector(generateRandomArray(NumFeatures))        val y: Double = w.dot(x)        val noisy = y + intercept //+ 0.1 * random.nextGaussian()        (noisy, x)      }    }    // create a network producer    val listener = new ServerSocket(9999)    println("Listening on port: 9999")    while (true) {      val socket = listener.accept()      new Thread() {        override def run = {          println("Got client connected from: " + socket.getInetAddress)          val out = new PrintWriter(socket.getOutputStream(), true)          while (true) {            Thread.sleep(1000)            val num = random.nextInt(MaxEvents)            val data = generateNoisyData(num)            data.foreach { case (y, x) =>              val xStr = x.data.mkString(",")              val eventStr = s"$y\t$xStr"              out.write(eventStr)              out.write("\n")            }            out.flush()            println(s"Created $num events...")          }          socket.close()        }      }.start()    }  }}

sbt runEnter number: 5

4.2 流回归模型

object SimpleStreamingModel {  def main(args: Array[String]) {    val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))    val stream = ssc.socketTextStream("localhost", 9999)    val NumFeatures = 100    val zeroVector = DenseVector.zeros[Double](NumFeatures)    val model = new StreamingLinearRegressionWithSGD()      .setInitialWeights(Vectors.dense(zeroVector.data))      .setNumIterations(1)      .setStepSize(0.01)    // create a stream of labeled points    val labeledStream: DStream[LabeledPoint] = stream.map { event =>      val split = event.split("\t")      val y = split(0).toDouble      val features: Array[Double] = split(1).split(",").map(_.toDouble)      LabeledPoint(label = y, features = Vectors.dense(features))    }    // train and test model on the stream, and print predictions for illustrative purposes    model.trainOn(labeledStream)    //model.predictOn(labeledStream).print()    ssc.start()    ssc.awaitTermination()  }}

sbt runEnter number: 5

5 流K-均值

K-均值聚类：StreamingKMeans

6 评估

object MonitoringStreamingModel {  def main(args: Array[String]) {    val ssc = new StreamingContext("local[2]", "First Streaming App", Seconds(10))    val stream = ssc.socketTextStream("localhost", 9999)    val NumFeatures = 100    val zeroVector = DenseVector.zeros[Double](NumFeatures)    val model1 = new StreamingLinearRegressionWithSGD()      .setInitialWeights(Vectors.dense(zeroVector.data))      .setNumIterations(1)      .setStepSize(0.01)    val model2 = new StreamingLinearRegressionWithSGD()      .setInitialWeights(Vectors.dense(zeroVector.data))      .setNumIterations(1)      .setStepSize(1.0)    // create a stream of labeled points    val labeledStream = stream.map { event =>      val split = event.split("\t")      val y = split(0).toDouble      val features = split(1).split(",").map(_.toDouble)      LabeledPoint(label = y, features = Vectors.dense(features))    }    // train both models on the same stream    model1.trainOn(labeledStream)    model2.trainOn(labeledStream)    // use transform to create a stream with model error rates    val predsAndTrue = labeledStream.transform { rdd =>      val latest1 = model1.latestModel()      val latest2 = model2.latestModel()      rdd.map { point =>        val pred1 = latest1.predict(point.features)        val pred2 = latest2.predict(point.features)        (pred1 - point.label, pred2 - point.label)      }    }    // print out the MSE and RMSE metrics for each model per batch    predsAndTrue.foreachRDD { (rdd, time) =>      val mse1 = rdd.map { case (err1, err2) => err1 * err1 }.mean()      val rmse1 = math.sqrt(mse1)      val mse2 = rdd.map { case (err1, err2) => err2 * err2 }.mean()      val rmse2 = math.sqrt(mse2)      println(        s"""           |-------------------------------------------           |Time: $time           |-------------------------------------------         """.stripMargin)      println(s"MSE current batch: Model 1: $mse1; Model 2: $mse2")      println(s"RMSE current batch: Model 1: $rmse1; Model 2: $rmse2")      println("...\n")    }    ssc.start()    ssc.awaitTermination()  }}

sbt runEnter number: 1