Spark MLlib平台的协同过滤算法---电影推荐系统学习

import org.apache.log4j.{Level, Logger}
import org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}
import org.apache.spark.rdd._
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.SparkContext._
 
import scala.io.Source
 
object MovieLensALS {
  def main(args:Array[String]) {
 
    // 屏蔽不必要的日志显示在终端上
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.apache.eclipse.jetty.server").setLevel(Level.OFF)
 
    // 设置运行环境
    val sparkConf = new SparkConf().setAppName("MovieLensALS").setMaster("local[5]")
    val sc = new SparkContext(sparkConf)
 
    //装载用户评分，该评分由评分器生成(即生成文件personalRatings.txt)
    val myRatings = loadRatings(args(1))
    val myRatingsRDD = sc.parallelize(myRatings, 1)
 
    //样本数据目录
    val movielensHomeDir = args(0)
 
    //装载样本评分数据，其中最后一列Timestamp取除10的余数作为key，Rating为值，即(Int，Rating)
    val ratings = sc.textFile(movielensHomeDir + "/ratings.dat").map {
      line =>
        val fields = line.split("::")
        // 格式 -> (timestamp % 10, Rating(userId, movieId, rating))
        (fields(3).toLong % 10, Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble))
    }
 
    //装载电影目录对照表(电影ID -> 电影标题)
    val movies = sc.textFile(movielensHomeDir + "/movies.dat").map {
      line =>
        val fields = line.split("::")
        // 格式 --> (movieId, movieName)
        (fields(0).toInt, fields(1))
    }.collect().toMap
    
    // 统计用户数量和电影数量以及用户对电影的评分数目
    val numRatings = ratings.count()
    val numUsers = ratings.map(_._2.user).distinct().count()
    val numMovies = ratings.map(_._2.product).distinct().count()
    println("Got " + numRatings + " ratings from " + numUsers + " users " + numMovies + " movies")
 
    // 将样本评分表以key值切分成3个部分，分别用于训练 (60%，并加入用户评分), 校验 (20%), and 测试 (20%)
    //该数据在计算过程中要多次应用到，所以cache到内存
    val numPartitions = 4
    val training = ratings.filter(x => x._1 < 6).values.union(myRatingsRDD).repartition(numPartitions).persist()
    val validation = ratings.filter(x => x._1 >= 6 && x._1 < 8).values.repartition(numPartitions).persist()
    val test = ratings.filter(x => x._1 >= 8).values.persist()
 
    val numTraining = training.count()
    val numValidation = validation.count()
    val numTest = test.count()
    println("Training: " + numTraining + " validation: " + numValidation + " test: " + numTest)

    // 训练不同参数下的模型，并在校验集中验证，获取最佳参数下的模型
    val ranks = List(8, 12)
    val lambdas = List(0.1, 10.0)
    val numIters = List(10, 20)
    var bestModel: Option[MatrixFactorizationModel] = None
    var bestValidationRmse = Double.MaxValue
    var bestRank = 0
    var bestLambda = -1.0
    var bestNumIter = -1
 
    for (rank <- ranks; lambda <- lambdas; numIter <- numIters) {
      val model = ALS.train(training, rank, numIter, lambda)
      val validationRmse = computeRmse(model, validation, numValidation)
      println("RMSE(validation) = " + validationRmse + " for the model trained with rank = "
        + rank + ",lambda = " + lambda + ",and numIter = " + numIter + ".")
 
      if (validationRmse < bestValidationRmse) {
        bestModel = Some(model)
        bestValidationRmse = validationRmse
        bestRank = rank
        bestLambda = lambda
        bestNumIter = numIter
      }
    }
 
    // 用最佳模型预测测试集的评分，并计算和实际评分之间的均方根误差（RMSE）
    val testRmse = computeRmse(bestModel.get, test, numTest)
    println("The best model was trained with rank = " + bestRank + " and lambda = " + bestLambda
      + ", and numIter = " + bestNumIter + ", and its RMSE on the test set is " + testRmse + ".")
 
    //create a naive baseline and compare it with the best model
    val meanRating = training.union(validation).map(_.rating).mean
    val baselineRmse = math.sqrt(test.map(x => (meanRating - x.rating) * (meanRating - x.rating)).reduce(_ + _) / numTest)
    val improvement = (baselineRmse - testRmse) / baselineRmse * 100
    println("The best model improves the baseline by " + "%1.2f".format(improvement) + "%.")
 
    //  推荐前十部最感兴趣的电影，注意要剔除用户已经评分的电影
    val myRatedMovieIds = myRatings.map(_.product).toSet
    val candidates = sc.parallelize(movies.keys.filter(!myRatedMovieIds.contains(_)).toSeq)
    val recommendations = bestModel.get
      .predict(candidates.map((0, _)))
      .collect
      // 从大到小排列
      .sortBy(-_.rating)
      .take(10)
    var i = 1
    println("Movies recommended for you:")
    recommendations.foreach { r =>
      println("%2d".format(i) + ": " + movies(r.product))
      i += 1
    }
 
    sc.stop()
  }
 
 
  // 校验集预测数据和实际数据之间的均方根误差
  def computeRmse(model:MatrixFactorizationModel,data:RDD[Rating],n:Long):Double = {
   val predictions:RDD[Rating] = model.predict((data.map(x => (x.user,x.product))))
    val predictionsAndRatings = predictions.map{ x =>((x.user,x.product),x.rating)}
                          .join(data.map(x => ((x.user,x.product),x.rating))).values
    math.sqrt(predictionsAndRatings.map( x => (x._1 - x._2) * (x._1 - x._2)).reduce(_+_)/n)
  }
 
  /** 装载用户评分文件 personalRatings.txt **/
  def loadRatings(path:String):Seq[Rating] = {
    val lines = Source.fromFile(path).getLines()
    val ratings = lines.map{
      line =>
        val fields = line.split("::")
        Rating(fields(0).toInt,fields(1).toInt,fields(2).toDouble)
    }.filter(_.rating > 0.0)
    if(ratings.isEmpty){
      sys.error("No ratings provided.")
    }else{
      ratings.toSeq
    }
  }
}

转自： http://m635674608.iteye.com/blog/2285683