Spark中组件Mllib的学习11之使用ALS对movieLens中一百万条（1M）数据集进行训练，并对输入的新用户数据进行电影推荐

更多代码请见：https://github.com/xubo245/SparkLearning

1解释
spark-1.5.2
数据集：http://grouplens.org/datasets/movielens/
一百万条（1M）
数据划分：
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%)

用多个参数训练模型，取训练最好的模型，然后再来推荐

2.代码：

package apache.spark.mllib.learning.recommendimport java.io.Fileimport scala.io.Sourceimport org.apache.log4j.{Level, Logger}import org.apache.spark.SparkConfimport org.apache.spark.SparkContextimport org.apache.spark.SparkContext._import org.apache.spark.rdd._import org.apache.spark.mllib.recommendation.{ALS, Rating, MatrixFactorizationModel}object MovieLensALSFromDataGuru {  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.eclipse.jetty.server").setLevel(Level.OFF)    //    if (args.length != 2) {    //      println("Usage: /path/to/spark/bin/spark-submit --driver-memory 2g --class week6.MovieLensALS " +    //        "target/scala-*/movielens-als-ssembly-*.jar movieLensHomeDir personalRatingsFile")    //      sys.exit(1)    //    }    //设置运行环境    val conf = new SparkConf().setAppName(this.getClass().getSimpleName().filter(!_.equals('$'))).setMaster("local[4]")    val sc = new SparkContext(conf)    //装载用户评分，该评分由评分器生成    //    val myRatings = loadRatings(args(1))    val myRatings = loadRatings("file/data/mllib/input/personalRatings.txt")    val myRatingsRDD = sc.parallelize(myRatings, 1)    //样本数据目录    //    val movieLensHomeDir = args(0)    val movieLensHomeDir = "file/data/mllib/input/movielens/medium/"    //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(new File(movieLensHomeDir, "ratings.dat").toString).map { line =>      val fields = line.split("::")      (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(new File(movieLensHomeDir, "movies.dat").toString).map { line =>      val fields = line.split("::")      (fields(0).toInt, fields(1))    }.collect().toMap    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 on " + 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) //注意ratings是(Int,Rating)，取value即可      .repartition(numPartitions)      .cache()    val validation = ratings.filter(x => x._1 >= 6 && x._1 < 8)      .values      .repartition(numPartitions)      .cache()    val test = ratings.filter(x => x._1 >= 8).values.cache()    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    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)).mean)    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()      .sortBy(-_.rating)      .take(10)    var i = 1    println("Movies recommended for you:")    recommendations.foreach { r =>      println("%2d".format(i) + ": " + movies(r.product))      i += 1    }    println("end")    //结束    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 **/  def loadRatings(path: String): Seq[Rating] = {    val lines = Source.fromFile(path).getLines()    val ratings = lines.map { line =>      val fields = line.split("::")      fields(2)=2.toString      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    }  }}

个人输入数据：

0::1::?::1400000000::Toy Story (1995)0::780::?::1400000000::Independence Day (a.k.a. ID4) (1996)0::590::?::1400000000::Dances with Wolves (1990)0::1210::?::1400000000::Star Wars: Episode VI - Return of the Jedi (1983)0::648::?::1400000000::Mission: Impossible (1996)0::344::?::1400000000::Ace Ventura: Pet Detective (1994)0::165::?::1400000000::Die Hard: With a Vengeance (1995)0::153::?::1400000000::Batman Forever (1995)0::597::?::1400000000::Pretty Woman (1990)0::1580::?::1400000000::Men in Black (1997)0::231::?::1400000000::Dumb & Dumber (1994)

3.结果：

D:\1win7\java\jdk\bin\java -Didea.launcher.port=7532 "-Didea.launcher.bin.path=D:\1win7\idea\IntelliJ IDEA Community Edition 15.0.4\bin" -Dfile.encoding=UTF-8 -classpath "D:\all\idea\SparkLearning\target\classes;D:\1win7\java\jdk\jre\lib\charsets.jar;D:\1win7\java\jdk\jre\lib\deploy.jar;D:\1win7\java\jdk\jre\lib\ext\access-bridge-64.jar;D:\1win7\java\jdk\jre\lib\ext\dnsns.jar;D:\1win7\java\jdk\jre\lib\ext\jaccess.jar;D:\1win7\java\jdk\jre\lib\ext\localedata.jar;D:\1win7\java\jdk\jre\lib\ext\sunec.jar;D:\1win7\java\jdk\jre\lib\ext\sunjce_provider.jar;D:\1win7\java\jdk\jre\lib\ext\sunmscapi.jar;D:\1win7\java\jdk\jre\lib\ext\zipfs.jar;D:\1win7\java\jdk\jre\lib\javaws.jar;D:\1win7\java\jdk\jre\lib\jce.jar;D:\1win7\java\jdk\jre\lib\jfr.jar;D:\1win7\java\jdk\jre\lib\jfxrt.jar;D:\1win7\java\jdk\jre\lib\jsse.jar;D:\1win7\java\jdk\jre\lib\management-agent.jar;D:\1win7\java\jdk\jre\lib\plugin.jar;D:\1win7\java\jdk\jre\lib\resources.jar;D:\1win7\java\jdk\jre\lib\rt.jar;D:\1win7\scala;D:\1win7\scala\lib;D:\1win7\java\otherJar\spark-assembly-1.5.2-hadoop2.6.0.jar;D:\1win7\java\otherJar\adam-apis_2.10-0.18.3-SNAPSHOT.jar;D:\1win7\java\otherJar\adam-cli_2.10-0.18.3-SNAPSHOT.jar;D:\1win7\java\otherJar\adam-core_2.10-0.18.3-SNAPSHOT.jar;D:\1win7\java\otherJar\SparkCSV\com.databricks_spark-csv_2.10-1.4.0.jar;D:\1win7\java\otherJar\SparkCSV\com.univocity_univocity-parsers-1.5.1.jar;D:\1win7\java\otherJar\SparkCSV\org.apache.commons_commons-csv-1.1.jar;D:\1win7\java\otherJar\SparkAvro\spark-avro_2.10-2.0.1.jar;D:\1win7\java\otherJar\SparkAvro\spark-avro_2.10-2.0.1-javadoc.jar;D:\1win7\java\otherJar\SparkAvro\spark-avro_2.10-2.0.1-sources.jar;D:\1win7\java\otherJar\avro\spark-avro_2.10-2.0.2-SNAPSHOT.jar;D:\1win7\java\otherJar\tachyon\tachyon-assemblies-0.7.1-jar-with-dependencies.jar;D:\1win7\scala\lib\scala-actors-migration.jar;D:\1win7\scala\lib\scala-actors.jar;D:\1win7\scala\lib\scala-library.jar;D:\1win7\scala\lib\scala-reflect.jar;D:\1win7\scala\lib\scala-swing.jar;C:\Users\xubo\.m2\repository\com\github\scopt\scopt_2.10\3.2.0\scopt_2.10-3.2.0.jar;C:\Users\xubo\.m2\repository\org\scala-lang\scala-library\2.10.3\scala-library-2.10.3.jar;D:\1win7\idea\IntelliJ IDEA Community Edition 15.0.4\lib\idea_rt.jar" com.intellij.rt.execution.application.AppMain apache.spark.mllib.learning.recommend.MovieLensALSFromDataGuruSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/D:/1win7/java/otherJar/spark-assembly-1.5.2-hadoop2.6.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/D:/1win7/java/otherJar/adam-cli_2.10-0.18.3-SNAPSHOT.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/D:/1win7/java/otherJar/tachyon/tachyon-assemblies-0.7.1-jar-with-dependencies.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]2016-05-17 21:57:32 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable2016-05-17 21:57:34 WARN  MetricsSystem:71 - Using default name DAGScheduler for source because spark.app.id is not set.2016-05-17 21:57:36 WARN  :139 - Your hostname, xubo-PC resolves to a loopback/non-reachable address: fe80:0:0:0:200:5efe:d356:9f8e%20, but we couldn't find any external IP address!Got 1000209 ratings from 6040 users on 3706 movies.[Stage 10:=============================>                            (1 + 1) / 2]Training: 602252, validation: 198919, test: 1990492016-05-17 21:57:51 WARN  BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS2016-05-17 21:57:51 WARN  BLAS:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS2016-05-17 21:57:51 WARN  LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK2016-05-17 21:57:51 WARN  LAPACK:61 - Failed to load implementation from: com.github.fommil.netlib.NativeRefLAPACKRMSE (validation) = 0.8781453642109673 for the model trained with rank = 8, lambda = 0.1, and numIter = 10.RMSE (validation) = 0.8726907141829461 for the model trained with rank = 8, lambda = 0.1, and numIter = 20.RMSE (validation) = 3.7558695311242833 for the model trained with rank = 8, lambda = 10.0, and numIter = 10.RMSE (validation) = 3.7558695311242833 for the model trained with rank = 8, lambda = 10.0, and numIter = 20.RMSE (validation) = 0.8782579367891573 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.[Stage 1228:==========================================>             (3 + 1) / 4]RMSE (validation) = 0.8708994704769754 for the model trained with rank = 12, lambda = 0.1, and numIter = 20.RMSE (validation) = 3.7558695311242833 for the model trained with rank = 12, lambda = 10.0, and numIter = 10.[Stage 1634:==============>                                         (1 + 3) / 4]RMSE (validation) = 3.7558695311242833 for the model trained with rank = 12, lambda = 10.0, and numIter = 20.[Stage 1687:==========================================>             (3 + 1) / 4]The best model was trained with rank = 12 and lambda = 0.1, and numIter = 20, and its RMSE on the test set is 0.868840371932273.The best model improves the baseline by 21.97%.Movies recommended for you: 1: Bandits (1997) 2: Very Thought of You, The (1998) 3: Ayn Rand: A Sense of Life (1997) 4: Shawshank Redemption, The (1994) 5: First Love, Last Rites (1997) 6: Matrix, The (1999) 7: Bewegte Mann, Der (1994) 8: Raiders of the Lost Ark (1981) 9: Big Trees, The (1952)10: Die Hard (1988)endProcess finished with exit code 0

参考
【1】http://spark.apache.org/docs/1.5.2/mllib-guide.html
【2】http://spark.apache.org/docs/1.5.2/mllib-collaborative-filtering.html#collaborative-filtering
【3】https://github.com/xubo245/SparkLearning