Spark LDA實戰

選取了10個文檔，其中4個來自於一篇論文，3篇來自於一篇新聞，3篇來自於另一篇新聞。

main函數：

  def main(args:Array[String]): Unit ={
    // 連接MySQL資料庫
    case class Article(id:Int,article:String)
    case class Word(id:Int,word:String)
    case class Vocabulary(id:Int,word_id:Int,article_id:Int,count:Int)
//    Class.forName("com.mysql.jdbc.Driver")
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    import sqlContext.implicits._
    val url = "jdbc:mysql://192.168.1.101:3306/machinelearning"
    val table_article = "article"
    val table_word = "word"
    val table_vocabulary = "vocabulary"
    val table_stopword = "stopword"
    val reader = sqlContext.read.format("jdbc")
    reader.option("url", url)
    reader.option("driver", "com.mysql.jdbc.Driver")
    reader.option("user", "hadoop")
    reader.option("password", "root")
    reader.option("dbtable", table_article)
    val df_article = reader.load()
    reader.option("dbtable", table_word)
    val df_word = reader.load()
    reader.option("dbtable", table_vocabulary)
    val df_vocabulary = reader.load()
    var vocuRow = df_vocabulary.collect()
    reader.option("dbtable", table_stopword)
    val df_stopword = reader.load()
    var stopRow = df_stopword.collect()

    // 記錄屬性
    val prop = new java.util.Properties
    prop.setProperty("user", "hadoop")
    prop.setProperty("password", "root")
    prop.setProperty("driver", "com.mysql.jdbc.Driver")

    // 讀取文檔的單詞到vocabulary表
　　insertVocabulary("hdfs://master:9000/ml/data/Article_01.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_02.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_03.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_04.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_05.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_06.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_07.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_08.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_09.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()
　　insertVocabulary("hdfs://master:9000/ml/data/Article_10.txt", vocuRow)
　　vocuRow = df_vocabulary.collect()

    // 插入stopword表
　　insertStopWord("hdfs://master:9000/ml/data/stop_words_eng.txt")
　　stopRow = df_stopword.collect()

    // 從Vocabulary表中刪除StopWord表裡面的詞
    compareAndDelete(vocuRow, stopRow)
    vocuRow = df_vocabulary.collect()

    // 生成LDA用的RDD
    val ldaBufferArrayBuffer = ArrayBuffer[Array[Double]]()
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_01.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_02.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_03.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_04.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_05.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_06.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_07.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_08.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_09.txt", vocuRow)
    ldaBufferArrayBuffer += getFrequency("hdfs://master:9000/ml/data/Article_10.txt", vocuRow)

    val parsedLdaArray = ldaBufferArrayBuffer.toArray.map(s => Vectors.dense(s))
    val ldaRDD = sc.parallelize(parsedLdaArray).zipWithIndex().map(_.swap).cache()

    println("LDA is training...")
    val ldaModel = new LDA()
      // 主題數量
      .setK(3)
      // 超參數(偽計數)
      .setDocConcentration(1.1)
      // 超參數(偽計數)
      .setTopicConcentration(1.1)
      // 最大迭代次數
      .setMaxIterations(100)
      .setSeed(0L)
      .setCheckpointInterval(10)
      .setOptimizer("em")
      .run(ldaRDD)
    println("LDA finishes training...")

    println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize + " words):")
    // 主題分佈
    val topics = ldaModel.topicsMatrix
    try {
      for (topic <- Range(0, 3)) {
        print("Topic " + topic + ":")
        for (word <- Range(0, ldaModel.vocabSize)) {
          print(" " + word + "(" + topics(word, topic) + ")");
        }
        println()
      }
    } catch {
      case ex : Exception => println(ex)
    }

    // 主題分佈排序
    val topics2 = ldaModel.describeTopics(10)
    println("The topics described by their top-weighted terms:")
    try {
      for (topic2 <- Range(0, topics2.length)) {
        print("Topic " + topic2 + ":")
        for (word <- Range(0, topics2.apply(topic2)._1.size)) {
          print(word + "(" + topics2.apply(topic2)._2.apply(word) + ") ")
        }
        println
      }
    } catch {
      case ex : Exception => println(ex)
    }

    // 文檔分佈
    println("The documentation distribution:")
    val distLDAModel = ldaModel.asInstanceOf[DistributedLDAModel]
    val docs = distLDAModel.topicDistributions.collect
    val docs2 = docs.sortBy(_._1)
    try{
      for (doc <- Range(0, docs2.length)){
          println(docs2.apply(doc)._1 + " " + docs2.apply(doc)._2)
      }
    } catch {
      case ex : Exception => println(ex)
    }

    sc.stop
  }

統計每個文檔的每個單詞出現的次數:

  def getFrequency(path : String, recordRow : Array[Row]) : Array[Double] = {
    println(path + "'s frequency is getting...")
    val res = ArrayBuffer[Double]()
    val textRDD = sc.textFile(path)
    val wordRDD = textRDD.flatMap(line => line.split("\\s+"))
      .map(word => {
        // 對word進行處理,包括去除符號,轉換成小寫等
        var s = ""
        word.foreach(c => {
          var ch = c
          if (ch.isLetter) {
            if (Character.isUpperCase(ch)) {
              ch = ch.toLower
            }
            s = s + ch
          }
        })
        (s, 1)})
      .reduceByKey((x, y) => x + y)
      .sortByKey(true)
      .collect()

    for (i <- Range(0,recordRow.size)){
      try {
        val r = recordRow.apply(i).getString(1)
        var isFound = false
        breakable {
          wordRDD.foreach(w => {
            if (r.equals(w._1)) {
              res += w._2.toDouble
              isFound = true
              break
            }
          })
        }
        if (!isFound)
          res += 0.0
      } catch {
        case ex : Exception => println(ex)
      }
    }

    println("Frequency finishes geting!")
    res.toArray
  }

從Vocabulary表中刪除StopWord表裡面的詞:

  def compareAndDelete(vRow : Array[Row], sRow : Array[Row]) : Unit = {
    println("CompareAndDelete is running...")
    var conn: Connection = null
    var ps: PreparedStatement = null
    val sql = "delete from vocabulary where Word = ?"
    conn = DriverManager.getConnection("jdbc:mysql://192.168.1.101:3306/machinelearning", "hadoop", "root")
    val vocuArray = new ArrayBuffer[String]()
    val stopArray = new ArrayBuffer[String]()
    val delArray = new ArrayBuffer[String]()
    vRow.foreach(v => {
      vocuArray += v.getString(1)
    })
    sRow.foreach(s => {
      stopArray += s.getString(1)
    })
    vocuArray.foreach(v => {
      stopArray.foreach(s => {
        if (v.equals(s))
          delArray += v
      })
    })
    delArray.foreach(d => {
      ps = conn.prepareStatement(sql)
      ps.setString(1, d)
      ps.executeUpdate()
    })
    println("CompareAndDelete finishes running!")
  }

把停用詞插入stopword表：

  def insertStopWord(path : String): Unit = {
    println("Stopword is inserting...")
    val textRDD = sc.textFile(path)
    val stopWordRDD = textRDD.flatMap(line => line.split("\\s+"))
      .map(word => (word, 1))
      .reduceByKey((x, y) => x + y)
      .sortByKey(true)

    // 寫入vocabulary表
    var conn: Connection = null
    var ps: PreparedStatement = null
    val sql = "insert into stopword(Word) values (?)"
    conn = DriverManager.getConnection("jdbc:mysql://192.168.1.101:3306/machinelearning", "hadoop", "root")
    stopWordRDD.collect().foreach(p => {
      // 該單詞寫入表vocabulary
      ps = conn.prepareStatement(sql)
      ps.setString(1, p._1)
      ps.executeUpdate()
    })
    println("Stopword finishes inserting!")

  }

把文檔的單詞插入vocabulary表：

  def insertVocabulary(path : String, recordRow : Array[Row]): Unit = {
    println(path + " is inserting...")
    val textRDD = sc.textFile(path)
    val wordRDD = textRDD.flatMap(line => line.split("\\s+"))
      .map(word => {
        // 對word進行處理,包括去除符號,轉換成小寫等
        var s = ""
        word.foreach(c => {
          var ch = c
          if (ch.isLetter) {
            if (Character.isUpperCase(ch)) {
              ch = ch.toLower
            }
            s = s + ch
          }
        })
        (s, 1)})
      .reduceByKey((x, y) => x + y)
    .sortByKey(true)

    // 寫入vocabulary表
    var conn: Connection = null
    var ps: PreparedStatement = null
    val sql = "insert into vocabulary(Word) values (?)"
    conn = DriverManager.getConnection("jdbc:mysql://192.168.1.101:3306/machinelearning", "hadoop", "root")
    wordRDD.collect().foreach(p => {
      println(p)
      var isWrite = true
      breakable{
        recordRow.foreach(q => {
          if (p._1 == q.getString(1)){
            // 已經存在這個單詞則不寫入
            isWrite = false
            break
          }
        }
        )
      }
      // 該單詞寫入表vocabulary
      if (isWrite){
        ps = conn.prepareStatement(sql)
        ps.setString(1, p._1)
        ps.executeUpdate()
      }
    })
    println(path + " finishes inserting!")
  }

運行結果：

Learned topics (as distributions over vocab of 1324 words):
Topic 0: 0(2.5145205580836416E-5) 1(2.5145205580836416E-5) 2(1.1291049227313227E-4) 3(2.5145205580836416E-5) 4(0.998745917983795) 5(2.5145205580836416E-5) 6(2.5145205580836416E-5) 7(2.5145205580836416E-5) 8(2.5145205580836416E-5) 9(0.9997669538548448) 10(2.7449336165991658E-5) 11(2.9975368739414727) 12(0.9996985895091448) 13(2.5145205580836416E-5) 14(4.936699969890295E-5) 15(9.273504406339597E-5) 16(2.5145205580836416E-5) 17(2.000014204168829) 18(2.5145205580836416E-5) 19(0.9991911071252098) 20(4.936699969890295E-5) 21(0.9985711249060892) 22(0.9993494841023468) 23(2.6342996410253656E-5) 24(2.676801478412973E-5) 25(2.998715335627293) 26(0.9997669538548448) 27(2.5145205580836416E-5) 28(2.5145205580836416E-5) 29(0.9993494841023468) 30(2.5145205580836416E-5) 31(2.5145205580836416E-5) 32(3.979229408939385E-5) 33(5.113882938317321E-5) 34(2.5145205580836416E-5) 35(2.5145205580836416E-5) 36(2.5145205580836416E-5) 37(2.6342996410253656E-5) 38(2.5145205580836416E-5) 39(2.9998155642939417) 40(4.999433111714826) 41(3.387405036548482E-5) 42(2.5145205580836416E-5) 43(3.979229408939385E-5) 44(5.973997183679773E-5) 45(2.5145205580836416E-5) 46(2.5145205580836416E-5) 47(4.936699969890295E-5) 48(2.5145205580836416E-5) 49(4.000545295615069) 50(2.5145205580836416E-5) 51(2.5145205580836416E-5) 52(2.5145205580836416E-5) 53(2.5145205580836416E-5) 54(2.6342996410253656E-5) 55(2.5145205580836416E-5) 56(2.5145205580836416E-5) 57(3.0003584697399495) 58(2.5145205580836416E-5) 59(5.056468298445796E-5) 60(2.5145205580836416E-5) 61(2.5145205580836416E-5) 62(2.5145205580836416E-5) 63(2.5145205580836416E-5) 64(0.9993494841023468) 65(0.9993494841023468) 66(2.5145205580836416E-5) 67(6.997591263562541) 68(2.5145205580836416E-5) 69(2.5145205580836416E-5) 70(0.9990237539827164) 71(2.5145205580836416E-5) 72(2.5145205580836416E-5) 73(2.5145205580836416E-5) 74(2.5145205580836416E-5) 75(3.587854320991863E-5) 76(2.6342996410253656E-5) 77(3.587854320991863E-5) 78(2.5145205580836416E-5) 79(2.5145205580836416E-5) 80(0.9997669538548448) 81(2.5145205580836416E-5) 82(2.5145205580836416E-5) 83(2.5145205580836416E-5) 84(3.0020395447940484) 85(2.5145205580836416E-5) 86(6.401160673356124E-5) 87(1.9981605849279473) 88(7.081780287742211E-5) 89(2.5145205580836416E-5) 90(1.0796362026414354E-4) 91(2.5145205580836416E-5) 92(2.5145205580836416E-5) 93(2.5145205580836416E-5) 94(2.5145205580836416E-5) 95(6.401160673356124E-5) 96(2.6342996410253656E-5) 97(4.499085544043795E-5) 98(5.276346455586639E-5) 99(2.5145205580836416E-5) 100(2.5145205580836416E-5) 101(2.5145205580836416E-5) 102(2.6342996410253656E-5) 103(2.5145205580836416E-5) 104(2.5145205580836416E-5) 105(2.5145205580836416E-5) 106(4.936699969890295E-5) 107(2.5145205580836416E-5) 108(2.5145205580836416E-5) 109(4.000070835486271) 110(8.27125254893778E-5) 111(2.5145205580836416E-5) 112(4.000177459372059) 113(0.9985100590613034) 114(6.401160673356124E-5) 115(3.080557709345904E-5) 116(2.5145205580836416E-5) 117(2.698570609408248E-5) 118(2.6342996410253656E-5) 119(2.5145205580836416E-5) 120(2.5145205580836416E-5) 121(2.5145205580836416E-5) 122(7.001432387508433) 123(2.5145205580836416E-5) 124(2.676801478412973E-5) 125(2.6342996410253656E-5) 126(2.5145205580836416E-5) 127(2.5145205580836416E-5) 128(2.5145205580836416E-5) 129(0.9992394425337842) 130(2.5145205580836416E-5) 131(7.926846928146479E-5) 132(2.5145205580836416E-5) 133(4.936699969890295E-5) 134(2.5145205580836416E-5) 135(2.5145205580836416E-5) 136(0.9997405100542728) 137(2.5145205580836416E-5) 138(2.5145205580836416E-5) 139(0.999008000950605) 140(2.5145205580836416E-5) 141(2.5145205580836416E-5) 142(2.5145205580836416E-5) 143(2.5145205580836416E-5) 144(2.5145205580836416E-5) 145(5.056468298445796E-5) 146(2.5145205580836416E-5) 147(2.5145205580836416E-5) 148(2.5145205580836416E-5) 149(2.5145205580836416E-5) 150(2.5145205580836416E-5) 151(2.5145205580836416E-5) 152(2.6342996410253656E-5) 153(0.9953188882008727) 154(2.7270849902875906E-5) 155(2.6342996410253656E-5) 156(7.737822026514119E-5) 157(2.6342996410253656E-5) 158(2.5145205580836416E-5) 159(2.5145205580836416E-5) 160(2.6342996410253656E-5) 161(6.91451329402625E-5) 162(2.5145205580836416E-5) 163(2.5145205580836416E-5) 164(6.5453085305864E-5) 165(2.5145205580836416E-5) 166(1.9995297272663017) 167(2.000216218038889) 168(2.676801478412973E-5) 169(2.5145205580836416E-5) 170(2.731896045227702E-5) 171(12.010168692813501) 172(0.9981211283744875) 173(2.5145205580836416E-5) 174(2.5145205580836416E-5) 175(8.27125254893778E-5) 176(2.676801478412973E-5) 177(1.997622908860093) 178(4.936699969890295E-5) 179(5.153769989016098E-5) 180(2.5145205580836416E-5) 181(3.0002836115187366) 182(2.5145205580836416E-5) 183(1.999568052126521) 184(0.9997669538548448) 185(2.5145205580836416E-5) 186(17.007642787151525) 187(5.0520671517072685E-5) 188(2.676801478412973E-5) 189(2.5145205580836416E-5) 190(2.5145205580836416E-5) 191(5.001730677979211) 192(9.39351853021172E-5) 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