Spark数据挖掘实例1：基于 Audioscrobbler 数据集音乐推荐

本实例来源于《Spark高级数据分析》，这是一个很好的spark数据挖掘的实例。从经验上讲，推荐引擎属于大规模机器学习，在日常购物中大家或许深有体会，比如：你在淘宝上浏览了一些商品，或者购买了一些商品，那么淘宝就会根据你的偏好给你推荐一些其他类似的商品。然而，相比较其他机器学习算法，推荐引擎的输出更加的直观，有时候的推荐效果让人吃惊。作为机器学习开篇文章，本篇文章会系统的介绍基于Audioscrobbler数据集的音乐推荐。

数据集介绍

Audioscrobbler数据集是一个公开发布的数据集，读者可以在（https://github.com/libaoquan95/aasPractice/tree/master/c3/profiledata_06-May-2005）网站获取。数据集主要有三部分组成，user_artist_data.txt文件是主要的数据集文件记录了约2420条用户id、艺术家id以及用户收听艺术家歌曲的次数数据，包含141000个用户和160万个艺术家；artist_data.txt文件记录了艺术家id和对应的名字；artist_alias.txt记录了艺术家id和对应的别称id。

推荐算法介绍

由于所选取的数据集只记录了用户和歌曲之间的交互情况，除了艺术家名字之外没有其他信息。因此要找的学习算法不需要用户和艺术家的属性信息，这类算法通常被称为协同过滤。如果根据两个用户的年龄相同来判断他们可能具有相似的偏好，这不叫协同过滤。相反，根据两个用户播放过许多相同歌曲来判断他们可能都喜欢某首歌，这是协调过滤。

本篇所用的算法在数学上称为迭代最小二乘，把用户播放数据当成矩阵A，矩阵低i行第j列上的元素的值，代表用户i播放艺术家j的音乐。矩阵A是稀疏的，绝大多数元素是0，算法将A分解成两个小矩阵X和Y，既A=XYT，X代表用户特征矩阵，Y代表特征艺术家矩阵。两个矩阵的乘积当做用户-艺术家关系矩阵的估计。可以通过下边一组图直观的反映：

现在假如有5个听众，音乐有5首，那么A是一个5*5的矩阵，假如评分如下：

图2.1 用户订阅矩阵

假如d是三个属性，那么X的矩阵如下：

图2.2 用户-特征矩阵

Y的矩阵如下：

图2.3 特征-电影矩阵

实际的求解过程中通常先随机的固定矩阵Y，则，为提高计算效率，通常采用并行计算X的每一行，既。得到X之后，再反求出Y，不断的交替迭代，最终使得XYT与A的平方误差小于指定阈值，停止迭代，得到最终的X（代表用户特征矩阵）和Y矩阵（代表特征艺术家矩阵）。在根据最终X和Y矩阵结果，向用户进行推荐。

数据准备

首先将样例数据上传到HDFS，如果想要在本地测试这些功能的话，需要内存数量至少 6g，当然可以通过减少数据量来达到通用的测试。

object RunRecommender {def main(args: Array[String]): Unit = {val conf = new SparkConf();conf.setMaster("local[*]")val spark = SparkSession.builder().config(conf).getOrCreate()// Optional, but may help avoid errors due to long lineage// spark.sparkContext.setCheckpointDir("hdfs:///tmp/")spark.sparkContext.setCheckpointDir("d:///tmp/")//val base = "hdfs:///user/ds/"val base =  "E:/newcode/spark/aas/data/";val rawUserArtistData = spark.read.textFile(base + "user_artist_data.txt")val rawArtistData = spark.read.textFile(base + "artist_data.txt")val rawArtistAlias = spark.read.textFile(base + "artist_alias.txt")val runRecommender = new RunRecommender(spark)runRecommender.preparation(rawUserArtistData, rawArtistData, rawArtistAlias)runRecommender.model(rawUserArtistData, rawArtistData, rawArtistAlias)runRecommender.evaluate(rawUserArtistData, rawArtistAlias)runRecommender.recommend(rawUserArtistData, rawArtistData, rawArtistAlias)}}

def preparation(rawUserArtistData: Dataset[String],rawArtistData: Dataset[String],rawArtistAlias: Dataset[String]): Unit = {rawUserArtistData.take(5).foreach(println)val userArtistDF = rawUserArtistData.map { line =>val Array(user, artist, _*) = line.split(' ')(user.toInt, artist.toInt)}.toDF("user", "artist")userArtistDF.agg(min("user"), max("user"), min("artist"), max("artist")).show()val artistByID = buildArtistByID(rawArtistData)val artistAlias = buildArtistAlias(rawArtistAlias)val (badID, goodID) = artistAlias.headartistByID.filter($"id" isin (badID, goodID)).show()
}

/*** 过滤无效的用户艺术家ID和名字行，将格式不正确的数据行剔除掉。* @param rawArtistData* @return*/
def buildArtistByID(rawArtistData: Dataset[String]): DataFrame = {rawArtistData.flatMap { line =>val (id, name) = line.span(_ != '\t')if (name.isEmpty) {None} else {try {Some((id.toInt, name.trim))} catch {case _: NumberFormatException => None}}}.toDF("id", "name")
}/*** 过滤艺术家id和对应的别名id，将格式拼写错误的行剔除掉。* @param rawArtistAlias* @return*/
def buildArtistAlias(rawArtistAlias: Dataset[String]): Map[Int,Int] = {rawArtistAlias.flatMap { line =>val Array(artist, alias) = line.split('\t')if (artist.isEmpty) {None} else {Some((artist.toInt, alias.toInt))}}.collect().toMap
}

代码中模型训练好之后，预测了用户 2093760 的推荐结果，我测试结果如下，由于里面代码使用了随机生成初始矩阵，每个人的结果都有可能不一样。

Some((2814,50 Cent))
Some((829,Nas))
Some((1003249,Ludacris))
Some((1001819,2Pac))
Some((1300642,The Game))

代码中也给出了该用户以前听过的艺术家的名字如下：

Some((1180,David Gray))
Some((378,Blackalicious))
Some((813,Jurassic 5))
Some((1255340,The Saw Doctors))
Some((942,Xzibit))

模型评价

auc评价方法

def areaUnderCurve(positiveData: DataFrame,bAllArtistIDs: Broadcast[Array[Int]],predictFunction: (DataFrame => DataFrame)): Double = {// What this actually computes is AUC, per user. The result is actually something// that might be called "mean AUC".// Take held-out data as the "positive".// Make predictions for each of them, including a numeric scoreval positivePredictions = predictFunction(positiveData.select("user", "artist")).withColumnRenamed("prediction", "positivePrediction")// BinaryClassificationMetrics.areaUnderROC is not used here since there are really lots of// small AUC problems, and it would be inefficient, when a direct computation is available.// Create a set of "negative" products for each user. These are randomly chosen// from among all of the other artists, excluding those that are "positive" for the user.val negativeData = positiveData.select("user", "artist").as[(Int,Int)].groupByKey { case (user, _) => user }.flatMapGroups { case (userID, userIDAndPosArtistIDs) =>val random = new Random()val posItemIDSet = userIDAndPosArtistIDs.map { case (_, artist) => artist }.toSetval negative = new ArrayBuffer[Int]()val allArtistIDs = bAllArtistIDs.valuevar i = 0// Make at most one pass over all artists to avoid an infinite loop.// Also stop when number of negative equals positive set sizewhile (i < allArtistIDs.length && negative.size < posItemIDSet.size) {val artistID = allArtistIDs(random.nextInt(allArtistIDs.length))// Only add new distinct IDsif (!posItemIDSet.contains(artistID)) {negative += artistID}i += 1}// Return the set with user ID added backnegative.map(artistID => (userID, artistID))}.toDF("user", "artist")// Make predictions on the rest:val negativePredictions = predictFunction(negativeData).withColumnRenamed("prediction", "negativePrediction")// Join positive predictions to negative predictions by user, only.// This will result in a row for every possible pairing of positive and negative// predictions within each user.val joinedPredictions = positivePredictions.join(negativePredictions, "user").select("user", "positivePrediction", "negativePrediction").cache()// Count the number of pairs per userval allCounts = joinedPredictions.groupBy("user").agg(count(lit("1")).as("total")).select("user", "total")// Count the number of correctly ordered pairs per userval correctCounts = joinedPredictions.filter($"positivePrediction" > $"negativePrediction").groupBy("user").agg(count("user").as("correct")).select("user", "correct")// Combine these, compute their ratio, and average over all usersval meanAUC = allCounts.join(correctCounts, Seq("user"), "left_outer").select($"user", (coalesce($"correct", lit(0)) / $"total").as("auc")).agg(mean("auc")).as[Double].first()joinedPredictions.unpersist()meanAUC
}完整代码下载:RunRecommender.scala