Spark学习笔记——文本处理技术

1.建立TF-IDF模型

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.{SparseVector => SV}
import org.apache.spark.mllib.feature.HashingTF
import org.apache.spark.mllib.feature.IDF

/**
  * Created by common on 17-5-6.
  */
object TFIDF {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("WordCount").setMaster("local")
    val sc = new SparkContext(conf)

//    val path = "hdfs://master:9000/user/common/20Newsgroups/20news-bydate-train/*"
    val path = "file:///media/common/工作/kaggle/test/*"
    val rdd = sc.wholeTextFiles(path)

    // 提取文本信息
    val text = rdd.map { case (file, text) => text }
    //    print(text.count())

    val regex = """[^0-9]*""".r

    // 排除停用词
    val stopwords = Set(
      "the", "a", "an", "of", "or", "in", "for", "by", "on", "but", "is", "not",
      "with", "as", "was", "if",
      "they", "are", "this", "and", "it", "have", "from", "at", "my",
      "be", "that", "to"
    )

    // 以使用正则表达切分原始文档来移除这些非单词字符
    val nonWordSplit = text.flatMap(t =>
      t.split("""\W+""").map(_.toLowerCase))

    // 过滤掉数字和包含数字的单词
    val filterNumbers = nonWordSplit.filter(token =>
      regex.pattern.matcher(token).matches)

    // 基于出现的频率，排除很少出现的单词，需要先计算一遍整个测试集
    val tokenCounts = filterNumbers.map(t => (t, 1)).reduceByKey(_ + _)
    val rareTokens = tokenCounts.filter { case (k, v) => v < 2 }.map {
      case (k, v) => k
    }.collect.toSet

    // 每一个文档的预处理函数
    def tokenize(line: String): Seq[String] = {
      line.split("""\W+""")
        .map(_.toLowerCase)
        .filter(token => regex.pattern.matcher(token).matches)
        .filterNot(token => stopwords.contains(token))
        .filterNot(token => rareTokens.contains(token))
        .filter(token => token.size >= 2) //删除只有一个字母的单词
        .toSeq
    }

    // 每一篇文档经过预处理之后，每一个文档成为一个Seq[String]
    val tokens = text.map(doc => tokenize(doc)).cache()

    println(tokens.distinct.count)
    // 第一篇文档第一部分分词之后的结果
    println(tokens.first())
    println(tokens.first().length)

    // 生成2^18维的特征
    val dim = math.pow(2, 18).toInt
    val hashingTF = new HashingTF(dim)

    // HashingTF 的 transform 函数把每个输入文档(即词项的序列)映射到一个MLlib的Vector对象
    val tf = hashingTF.transform(tokens)
    // tf的长度是文档的个数，对应的是文档和维度的矩阵
    tf.cache

    // 取得第一个文档的向量
    val v = tf.first.asInstanceOf[SV]
    println(v.size)
    // v.value和v.indices的长度相等，value是词频，indices是词频非零的下标
    println(v.values.size)
    println(v.indices.size)
    println(v.values.toSeq)
    println(v.indices.take(10).toSeq)

    // 对每个单词计算逆向文本频率
    val idf = new IDF().fit(tf)
    // 转换词频向量为TF-IDF向量
    val tfidf = idf.transform(tf)
    val v2 = tfidf.first.asInstanceOf[SV]
    println(v2.values.size)
    println(v2.values.take(10).toSeq)
    println(v2.indices.take(10).toSeq)

    // 计算整个文档的TF-IDF最小和最大权值
    val minMaxVals = tfidf.map { v =>
      val sv = v.asInstanceOf[SV]
      (sv.values.min, sv.values.max)
    }
    val globalMinMax = minMaxVals.reduce { case ((min1, max1),
    (min2, max2)) =>
      (math.min(min1, min2), math.max(max1, max2))
    }
    println(globalMinMax)

    // 比较几个单词的TF-IDF权值
    val common = sc.parallelize(Seq(Seq("you", "do", "we")))
    val tfCommon = hashingTF.transform(common)
    val tfidfCommon = idf.transform(tfCommon)
    val commonVector = tfidfCommon.first.asInstanceOf[SV]
    println(commonVector.values.toSeq)

    val uncommon = sc.parallelize(Seq(Seq("telescope", "legislation","investment")))
    val tfUncommon = hashingTF.transform(uncommon)
    val tfidfUncommon = idf.transform(tfUncommon)
    val uncommonVector = tfidfUncommon.first.asInstanceOf[SV]
    println(uncommonVector.values.toSeq)

  }


}