Spark機器學習9· 實時機器學習(scala with sbt)

1 在線學習

模型隨着接收的新消息，不斷更新本身；而不是像離線訓練一次次從新訓練。

2 Spark Streaming

• 離散化流(DStream)

• 輸入源：Akka actors、消息隊列、Flume、Kafka、……
  

  http://spark.apache.org/docs/latest/streaming-programming-guide.html

• 類羣(lineage)：應用到RDD上的轉換算子和執行算子的集合

3 MLib+Streaming應用

3.0 build.sbt

依賴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" 

使用國內鏡像倉庫

~/.sbt/repositories

[repositories]
local
osc: 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], bootOnly sonatype-oss-releases maven-central sonatype-oss-snapshots 

3.1 生產消息

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 run

Multiple main classes detected, select one to run:

 [1] MonitoringStreamingModel [2] SimpleStreamingApp [3] SimpleStreamingModel [4] StreamingAnalyticsApp [5] StreamingModelProducer [6] StreamingProducer [7] StreamingStateApp Enter 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 run Enter 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 run Enter number: 4 

3.4 有狀態的流計算

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 run Enter number: 7 

4 線性流回歸

線性迴歸StreamingLinearRegressionWithSGD

• trainOn
• predictOn

4.1 流數據生成器

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 run Enter number: 5 

4.2 流回歸模型

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 run Enter number: 5 

5 流K-均值

• K-均值聚類：StreamingKMeans

6 評估

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 run Enter number: 1