Spark机器学习8· 文本处理(spark-shell)-阿里云开发者社区

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Spark机器学习8· 文本处理(spark-shell)

简介: ![](http://img3.douban.com/lpic/s28277325.jpg) [Spark机器学习](http://book.douban.com/subject/26593179/) 自然语言处理(NLP,Natural Language Processing) - 提取...


Spark机器学习

自然语言处理(NLP,Natural Language Processing)

  • 提取特征
  • 建模
  • 机器学习

TF-IDF(词频 term frequency–逆向文件频率 inverse document frequency)

  • 短语加权:根据词频,为单词赋予权值
  • 特征哈希:使用哈希方程对特征赋予向量下标

0 运行环境

tar xfvz 20news-bydate.tar.gz

export SPARK_HOME=/Users/erichan/Garden/spark-1.5.1-bin-hadoop2.6
cd $SPARK_HOME
bin/spark-shell --name my_mlib --packages org.jblas:jblas:1.2.4-SNAPSHOT --driver-memory 4G --executor-memory 4G --driver-cores 2

1 提取特征

val PATH = "/Users/erichan/sourcecode/book/Spark机器学习/20news-bydate"
val path = PATH+"/20news-bydate-train/*"
val rdd = sc.wholeTextFiles(path)
println(rdd.count)

11314

查看新闻组主题

val newsgroups = rdd.map { case (file, text) => file.split("/").takeRight(2).head }
val countByGroup = newsgroups.map(n => (n, 1)).reduceByKey(_ + _).collect.sortBy(-_._2).mkString("\n")
println(countByGroup)

(rec.sport.hockey,600)

(soc.religion.christian,599)
(rec.motorcycles,598)
(rec.sport.baseball,597)
(sci.crypt,595)
(rec.autos,594)
(sci.med,594)
(comp.windows.x,593)
(sci.space,593)
(sci.electronics,591)
(comp.os.ms-windows.misc,591)
(comp.sys.ibm.pc.hardware,590)
(misc.forsale,585)
(comp.graphics,584)
(comp.sys.mac.hardware,578)
(talk.politics.mideast,564)
(talk.politics.guns,546)
(alt.atheism,480)
(talk.politics.misc,465)
(talk.religion.misc,377)

2 建模

2.1 分词

val text = rdd.map { case (file, text) => text }
val whiteSpaceSplit = text.flatMap(t => t.split(" ").map(_.toLowerCase))
println(whiteSpaceSplit.distinct.count)
println(whiteSpaceSplit.sample(true, 0.3, 42).take(100).mkString(","))

402978
from:,mathew,mathew,faq:,faq:,atheist,resources

summary:,music,--,fiction,,mantis,consultants,,uk.
supersedes:,290

archive-name:,1.0

,,,,,,,,,,,,,,,,,,,organizations

,organizations

,,,,,,,,,,,,,,,,stickers,and,and,the,from,from,in,to:,to:,ffrf,,256-8900

evolution,designs

evolution,a,stick,cars,,written
inside.,fish,us.

write,evolution,,,,,,,bay,can,get,get,,to,the
price,is,of,the,the,so,on.,and,foote.,,atheist,pp.,0-910309-26-4,,,atrocities,,foote:,aap.,,the

2.2 改进分词

val nonWordSplit = text.flatMap(t => t.split("""\W+""").map(_.toLowerCase))
println(nonWordSplit.distinct.count)
println(nonWordSplit.distinct.sample(true, 0.3, 42).take(100).mkString(","))
val regex = """[^0-9]*""".r
val filterNumbers = nonWordSplit.filter(token => regex.pattern.matcher(token).matches)
println(filterNumbers.distinct.count)
println(filterNumbers.distinct.sample(true, 0.3, 42).take(100).mkString(","))

2.3 移除停用词

val tokenCounts = filterNumbers.map(t => (t, 1)).reduceByKey(_ + _)
val oreringDesc = Ordering.by[(String, Int), Int](_._2)
//println(tokenCounts.top(20)(oreringDesc).mkString("\n"))

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"
)
val tokenCountsFilteredStopwords = tokenCounts.filter { case (k, v) => !stopwords.contains(k) }
//println(tokenCountsFilteredStopwords.top(20)(oreringDesc).mkString("\n"))

val tokenCountsFilteredSize = tokenCountsFilteredStopwords.filter { case (k, v) => k.size >= 2 }
println(tokenCountsFilteredSize.top(20)(oreringDesc).mkString("\n"))

2.4 移除低频词

val oreringAsc = Ordering.by[(String, Int), Int](-_._2)
//println(tokenCountsFilteredSize.top(20)(oreringAsc).mkString("\n"))

val rareTokens = tokenCounts.filter{ case (k, v) => v < 2 }.map { case (k, v) => k }.collect.toSet
val tokenCountsFilteredAll = tokenCountsFilteredSize.filter { case (k, v) => !rareTokens.contains(k) }
println(tokenCountsFilteredAll.top(20)(oreringAsc).mkString("\n"))

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)
        .toSeq
}

//println(text.flatMap(doc => tokenize(doc)).distinct.count)
val tokens = text.map(doc => tokenize(doc))
println(tokens.first.take(20))

2.5 提取词干

  • 标准NLP方法
  • 搜索引擎

    • NLTK
    • OpenNLP
    • Lucene

3 训练模型

3.1 HashingTF 特征哈希

import org.apache.spark.mllib.linalg.{ SparseVector => SV }
import org.apache.spark.mllib.feature.HashingTF
import org.apache.spark.mllib.feature.IDF
// set the dimensionality of TF-IDF vectors to 2^18
val dim = math.pow(2, 18).toInt
val hashingTF = new HashingTF(dim)
val tf = hashingTF.transform(tokens)
tf.cache
val v = tf.first.asInstanceOf[SV]
println(v.size)
println(v.values.size)
println(v.values.take(10).toSeq)
println(v.indices.take(10).toSeq)

262144

706
WrappedArray(1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0)
WrappedArray(313, 713, 871, 1202, 1203, 1209, 1795, 1862, 3115, 3166)

fit & transform

val idf = new IDF().fit(tf)
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)

706
WrappedArray(2.3869085659322193, 4.670445463955571, 6.561295835827856, 4.597686109673142, 8.932700215224111, 5.750365619611528, 2.1871123786150006, 5.520408782213984, 3.4312512246662714, 1.7430324343790569)
WrappedArray(313, 713, 871, 1202, 1203, 1209, 1795, 1862, 3115, 3166)

3.2 分析权重

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)

globalMinMax: (Double, Double) = (0.0,66155.39470409753)

常用词

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)

WrappedArray(0.9965359935704624, 1.3348773448236835, 0.5457486182039175)

不常出现的单词

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)

WrappedArray(5.3265513728351666, 5.308532867332488, 5.483736956357579)

4 使用模型

4.1 余弦相似度

import breeze.linalg._

val hockeyText = rdd.filter { case (file, text) => file.contains("hockey") }
val hockeyTF = hockeyText.mapValues(doc => hashingTF.transform(tokenize(doc)))
val hockeyTfIdf = idf.transform(hockeyTF.map(_._2))

val hockey1 = hockeyTfIdf.sample(true, 0.1, 42).first.asInstanceOf[SV]
val breeze1 = new SparseVector(hockey1.indices, hockey1.values, hockey1.size)

val hockey2 = hockeyTfIdf.sample(true, 0.1, 43).first.asInstanceOf[SV]
val breeze2 = new SparseVector(hockey2.indices, hockey2.values, hockey2.size)
val cosineSim = breeze1.dot(breeze2) / (norm(breeze1) * norm(breeze2))
println(cosineSim)

cosineSim: Double = 0.060250114361164626

val graphicsText = rdd.filter { case (file, text) => file.contains("comp.graphics") }
val graphicsTF = graphicsText.mapValues(doc => hashingTF.transform(tokenize(doc)))
val graphicsTfIdf = idf.transform(graphicsTF.map(_._2))
val graphics = graphicsTfIdf.sample(true, 0.1, 42).first.asInstanceOf[SV]
val breezeGraphics = new SparseVector(graphics.indices, graphics.values, graphics.size)
val cosineSim2 = breeze1.dot(breezeGraphics) / (norm(breeze1) * norm(breezeGraphics))
println(cosineSim2)

cosineSim2: Double = 0.004664850323792852

val baseballText = rdd.filter { case (file, text) => file.contains("baseball") }
val baseballTF = baseballText.mapValues(doc => hashingTF.transform(tokenize(doc)))
val baseballTfIdf = idf.transform(baseballTF.map(_._2))
val baseball = baseballTfIdf.sample(true, 0.1, 42).first.asInstanceOf[SV]
val breezeBaseball = new SparseVector(baseball.indices, baseball.values, baseball.size)
val cosineSim3 = breeze1.dot(breezeBaseball) / (norm(breeze1) * norm(breezeBaseball))
println(cosineSim3)

0.05047395039466008

4.2 学习单词与主题的映射关系

多分类映射
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.evaluation.MulticlassMetrics

val newsgroupsMap = newsgroups.distinct.collect().zipWithIndex.toMap
val zipped = newsgroups.zip(tfidf)
val train = zipped.map { case (topic, vector) => LabeledPoint(newsgroupsMap(topic), vector) }
train.cache
朴素贝叶斯训练
val model = NaiveBayes.train(train, lambda = 0.1)
加载测试数据集
val testPath = PATH+"/20news-bydate-test/*"
val testRDD = sc.wholeTextFiles(testPath)
val testLabels = testRDD.map { case (file, text) =>
    val topic = file.split("/").takeRight(2).head
    newsgroupsMap(topic)
}
val testTf = testRDD.map { case (file, text) => hashingTF.transform(tokenize(text)) }
val testTfIdf = idf.transform(testTf)
val zippedTest = testLabels.zip(testTfIdf)
val test = zippedTest.map { case (topic, vector) => LabeledPoint(topic, vector) }
计算准确度和多分类加权F-指标
val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
println(accuracy)

0.7915560276155071

val metrics = new MulticlassMetrics(predictionAndLabel)
println(metrics.weightedFMeasure)

0.7810675969031116

5 评估

val rawTokens = rdd.map { case (file, text) => text.split(" ") }
val rawTF = rawTokens.map(doc => hashingTF.transform(doc))
val rawTrain = newsgroups.zip(rawTF).map { case (topic, vector) => LabeledPoint(newsgroupsMap(topic), vector) }
val rawModel = NaiveBayes.train(rawTrain, lambda = 0.1)
val rawTestTF = testRDD.map { case (file, text) => hashingTF.transform(text.split(" ")) }
val rawZippedTest = testLabels.zip(rawTestTF)
val rawTest = rawZippedTest.map { case (topic, vector) => LabeledPoint(topic, vector) }
val rawPredictionAndLabel = rawTest.map(p => (rawModel.predict(p.features), p.label))
val rawAccuracy = 1.0 * rawPredictionAndLabel.filter(x => x._1 == x._2).count() / rawTest.count()
println(rawAccuracy)

0.7648698884758365

val rawMetrics = new MulticlassMetrics(rawPredictionAndLabel)
println(rawMetrics.weightedFMeasure)

0.7653320418573546

6 Word2Vec模型

Word2Vec模型(分布向量表示):把每个单词表示成一个向量,MLlib中使用skip-gram模型

6.1 训练

import org.apache.spark.mllib.feature.Word2Vec
val word2vec = new Word2Vec()
word2vec.setSeed(42) // we do this to generate the same results each time
val word2vecModel = word2vec.fit(tokens)

6.2 使用

最相似的20个单词
word2vecModel.findSynonyms("hockey", 20).foreach(println)

(sport,1.4818968962277133)

(ecac,1.467546566194254)
(hispanic,1.4166835301985194)
(glens,1.4061103042432825)
(woofers,1.3810090447028116)
(tournament,1.3148823031671586)
(champs,1.3133863003013941)
(boxscores,1.307735040384543)
(aargh,1.274986851270267)
(ahl,1.265165428167253)
(playoff,1.2645991118770572)
(ncaa,1.2383382015648046)
(pool,1.2261154635870224)
(champion,1.2119919989539134)
(filinuk,1.2062208620660915)
(olympic,1.2026738930160243)
(motorcycles,1.2008032355579679)
(yankees,1.1989755767973371)
(calder,1.194001886835493)
(homeruns,1.1800625883573932)

word2vecModel.findSynonyms("legislation", 20).foreach(println)

(accommodates,0.9918184454068688)

(briefed,0.9256758135452989)
(amended,0.9105987267173344)
(telephony,0.8679173760123956)
(pitted,0.8609974033962533)
(aclu,0.8605885863332372)
(licensee,0.8493930472487975)
(agency,0.836706135804648)
(policies,0.8337986602365566)
(senate,0.8327312936220903)
(businesses,0.8291191155630467)
(permit,0.8266658804181389)
(cpsr,0.8231228090944367)
(cooperation,0.8195562469006543)
(surveillance,0.8134342524628756)
(congress,0.8132899468772855)
(restricted,0.8115013134507126)
(procure,0.8096839595766356)
(inquiry,0.8086297702914405)
(industry,0.8077900093754752)

  • legislation 立法
  • aclu 美国公民自由协会
  • senate 参议院
  • surveillance 监视
  • inquiry 调查

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