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.head
  artistByID.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 score
  val 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 }.toSet
      val negative = new ArrayBuffer[Int]()
      val allArtistIDs = bAllArtistIDs.value
      var i = 0
      // Make at most one pass over all artists to avoid an infinite loop.
      // Also stop when number of negative equals positive set size
      while (i < allArtistIDs.length && negative.size < posItemIDSet.size) {
        val artistID = allArtistIDs(random.nextInt(allArtistIDs.length))
        // Only add new distinct IDs
        if (!posItemIDSet.contains(artistID)) {
          negative += artistID
        }
        i += 1
      }
      // Return the set with user ID added back
      negative.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 user
  val allCounts = joinedPredictions.
    groupBy("user").agg(count(lit("1")).as("total")).
    select("user", "total")
  // Count the number of correctly ordered pairs per user
  val correctCounts = joinedPredictions.
    filter($"positivePrediction" > $"negativePrediction").
    groupBy("user").agg(count("user").as("correct")).
    select("user", "correct")

  // Combine these, compute their ratio, and average over all users
  val 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


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