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声明：代码主要以Scala为主，希望广大读者注意。本博客以代码为主，代码中会有详细的注释。相关文章将会发布在我的个人博客专栏《Spark 2.0机器学习》，欢迎大家关注。
在我的专栏《Python从入门到深度学习》中，已经介绍了两篇关于K-Means聚类的文章，分别是：《Python实现K-Means聚类算法》和《Python实现K-Means++聚类算法》。在此，K-Means算法的介绍及原理不再赘述，有兴趣的小伙伴可以点击查看我的这两篇相关文章。
接下来，一起来看一下，使用Scala实现K-Means算法的一个实例~
【数据形式】
数据的格式大体如下图所示：
图中可以看到，数据之间的分隔符为“|”，正常的数据都有8个字段。
【代码示例】
1、首先定义如下的样例类
//样例类
case class Doc(docId1: String, docId2: String, soure: String, name: String, docName: String, country: String, typeName: String, text: String)2、读取数据，分割并过滤掉不等于8个字段的数据
val docDataDF = spark.read.textFile(datapath + "test.txt")
.map(x => x.split("\\|"))
.filter(x => x.length == 8)
.map(doc => Doc(doc(0), doc(1), doc(2), doc(3), doc(4), doc(5), doc(6), doc(7)))3、定义正则表达式匹配
/**
* 去除URL、编码%0A、标点符号、数字
* @param s
* @return
*/
def replaceAndSplit(s: String): Array[String] = {
var s1 = s
s1 = s1.replaceAll("%\\w+\\w+|\\d+", "").trim
val targetList: String = ("""().,?[]!;|%*-""")
for (c <- (0 until targetList.length())){
s1 = s1.replace(targetList.charAt(c).toString, "")
}
val s2 = s1.split("\\,")
s2
}4、定义udf函数，是想上面的正则表达式匹配
/**
* udf 函数
*/
val clearData = udf{
(words: String) => replaceAndSplit(words)
}5、数据清洗，提取特征向量
/**
* 数据清洗，提取特征向量
* @param docDataDF
* @return 返回每一个单词对应的TF-IDF度量值
*/
def extractorByTFIDF(docDataDF: DataFrame): DataFrame = {
val sourceData = docDataDF.select("typeName", "text").toDF("label", "sentence")
//数据清洗
val doData_1 = sourceData.withColumn("vocabulary", clearData(col("sentence")))
//去除数组“[]”，转换为字符串
val doData_2 = doData_1.withColumn("words2", regexp_replace(doData_1.col("vocabulary").cast(StringType), "[\\['\\]]", ""))
// doData_2.select("label", "words2").show(5)
//将多个空格转换为一个空格
val doData_3 = doData_2.withColumn("words3", regexp_replace(doData_2.col("words2").cast(StringType), "\\s+", " "))
// doData_3.select("label", "words3").show(5)
val tokenizer = new Tokenizer()
.setInputCol("words3")
.setOutputCol("words")
val tokenized = tokenizer.transform(doData_3)
val tokenData = tokenized.select("label", "words")
//去停用词
val remover = new StopWordsRemover()
.setInputCol("words")
.setOutputCol("filtered")
val removerData = remover.transform(tokenData).select("label", "filtered")
// removerData.show(5, false)
val hashingTF = new HashingTF()
.setInputCol("filtered")
.setOutputCol("rawFeatures")
.setNumFeatures(100)
val featurizedData = hashingTF.transform(removerData)
val idf = new IDF()
.setInputCol("rawFeatures")
.setOutputCol("features")
val idfModel = idf.fit(featurizedData)
//得到每一个单词对应的TF-IDF度量值
val rescaleData = idfModel.transform(featurizedData).select("label", "features")
rescaleData
}6、使用K-Means模型进行计算
val kmeansmodel = new KMeans()
.setK(8)
.setFeaturesCol("features")
.setPredictionCol("prediction")
.fit(data)
val results = kmeansmodel.transform(data)
// results.show(false)
results.collect().foreach(row => {
println(row(0) + "is predicted as cluster" + row(2))
})
kmeansmodel.clusterCenters.foreach(
center => {
println("聚类中心：" + center)
}
)7、完整代码如下：
package sparkml
import org.apache.spark.ml.clustering.KMeans
import org.apache.spark.ml.feature.{HashingTF, IDF, StopWordsRemover, Tokenizer}
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.StringType
/**
* K-Means聚类测试
*/
//样例类
case class Doc(docId1: String, docId2: String, soure: String, name: String, docName: String, country: String, typeName: String, text: String)
object KMeansTest {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.appName("KMeansTest")
.master("local[*]")
.getOrCreate()
import spark.implicits._
//通过代码的方式，设置Spark log4j的级别
spark.sparkContext.setLogLevel("WARN")
val datapath = "./"
val docDataDF = spark.read.textFile(datapath + "test.txt")
.map(x => x.split("\\|"))
.filter(x => x.length == 8)
.map(doc => Doc(doc(0), doc(1), doc(2), doc(3), doc(4), doc(5), doc(6), doc(7)))
// docDataDF.show()
// docDataDF.cache()
//选取文档ID docId1、文档名称docName、文档类别typeName、文档内容text
val selectedData = docDataDF.select("docId1", "docName", "typeName", "text")
// selectedData.show(5)
//文档向量化
val data: DataFrame = extractorByTFIDF(selectedData)
val kmeansmodel = new KMeans()
.setK(8)
.setFeaturesCol("features")
.setPredictionCol("prediction")
.fit(data)
val results = kmeansmodel.transform(data)
// results.show(false)
results.collect().foreach(row => {
println(row(0) + "is predicted as cluster" + row(2))
})
kmeansmodel.clusterCenters.foreach(
center => {
println("聚类中心：" + center)
}
)
//KMeansModel类也提供了计算集合内误差平方和（Within Set Sum of Squared Error, WSSSE）的方法来度量聚类的有效性
//在真实k值未知的情况下，该值的变化可以作为选取合适k值的一个重要参考：输出本次聚类操作的收敛性，此值越低越好。
val WSSSE = kmeansmodel.computeCost(data)
println(s"Within Set Sum of Squared Errors = $WSSSE")
val dataDF = results.select("label", "prediction").toDF("docType", "docSort")
// dataDF.show()
dataDF.createOrReplaceTempView("doc_table")
val sqlstr = " select a.doctype, concat( round( a.typeTotal/b.total *100,2),'%') as ratio, a.docsort" +
" from ( select doctype, count(doctype) as typeTotal, docsort from doc_table group by doctype, docsort) a" +
" left join ( select count(doctype) as total, docsort from doc_table group by docsort) b" +
" on a.docsort = b.docsort group by ratio desc"
val distributionRatio = spark.sql(sqlstr)
distributionRatio.show(10, false)
}
/**
* 去除URL、编码%0A、标点符号、数字
* @param s
* @return
*/
def replaceAndSplit(s: String): Array[String] = {
var s1 = s
s1 = s1.replaceAll("%\\w+\\w+|\\d+", "").trim
val targetList: String = ("""().,?[]!;|%*-""")
for (c <- (0 until targetList.length())){
s1 = s1.replace(targetList.charAt(c).toString, "")
}
val s2 = s1.split("\\,")
s2
}
/**
* udf 函数
*/
val clearData = udf{
(words: String) => replaceAndSplit(words)
}
/**
* 数据清洗，提取特征向量
* @param docDataDF
* @return 返回每一个单词对应的TF-IDF度量值
*/
def extractorByTFIDF(docDataDF: DataFrame): DataFrame = {
val sourceData = docDataDF.select("typeName", "text").toDF("label", "sentence")
//数据清洗
val doData_1 = sourceData.withColumn("vocabulary", clearData(col("sentence")))
//去除数组“[]”，转换为字符串
val doData_2 = doData_1.withColumn("words2", regexp_replace(doData_1.col("vocabulary").cast(StringType), "[\\['\\]]", ""))
// doData_2.select("label", "words2").show(5)
//将多个空格转换为一个空格
val doData_3 = doData_2.withColumn("words3", regexp_replace(doData_2.col("words2").cast(StringType), "\\s+", " "))
// doData_3.select("label", "words3").show(5)
val tokenizer = new Tokenizer()
.setInputCol("words3")
.setOutputCol("words")
val tokenized = tokenizer.transform(doData_3)
val tokenData = tokenized.select("label", "words")
//去停用词
val remover = new StopWordsRemover()
.setInputCol("words")
.setOutputCol("filtered")
val removerData = remover.transform(tokenData).select("label", "filtered")
// removerData.show(5, false)
val hashingTF = new HashingTF()
.setInputCol("filtered")
.setOutputCol("rawFeatures")
.setNumFeatures(100)
val featurizedData = hashingTF.transform(removerData)
val idf = new IDF()
.setInputCol("rawFeatures")
.setOutputCol("features")
val idfModel = idf.fit(featurizedData)
//得到每一个单词对应的TF-IDF度量值
val rescaleData = idfModel.transform(featurizedData).select("label", "features")
rescaleData
}
}
程序运行结果如下：（因运行结果很多，这里只截取一部分结果）
Travel & Localis predicted as cluster2
Entertainmentis predicted as cluster7
Health & Fitnessis predicted as cluster7
Casualis predicted as cluster1
News & Magazinesis predicted as cluster1
Financeis predicted as cluster7
Libraries & Demois predicted as cluster0
Familyis predicted as cluster4
Media & Videois predicted as cluster1
News & Magazinesis predicted as cluster1
Businessis predicted as cluster1
Businessis predicted as cluster5
聚类中心：[0.9236032084506792,0.8284792431437666,0.7539312657453081,4.996626898033553,1.4495074538903303,0.8272722478638311,2.5614625099496355,2.337176093691245,0.7518750866288443,1.0467598190229594,0.7096854450851546,0.891150653870225,1.0956137690166972,1.2459433704084344,0.9034247433529918,0.8513513227695271,1.2520049107850049,1.14162279075498,1.928802361969307,1.0097444584659208,1.7309446207047765,1.0580077458248878,1.1499830608351467,0.933619876377747,1.6307734967683485,0.6083621015003998,1.0099863098613795,0.7477483495664728,1.2932130224844967,1.1030185844927236,0.6817343285512232,0.9922456004254353,1.1544606242809978,0.7604353584469028,0.9636219641268766,1.2272784842018238,0.9088400554326204,0.7085003758166575,1.0026934149732338,1.3702309828553951,0.9152828400553478,1.0184394979515818,0.6722967560137725,1.1161113189732355,0.7178264705485822,1.7196685892316244,1.1249367325734265,0.8479819431339212,1.187403341946672,0.6952589786329323,0.8069736108957462,0.7185549352890377,0.867267503224848,1.084255649342063,1.0340815976757587,0.8739926492406918,1.2839676297795128,1.377182302649207,0.8768118713866994,0.7915800684571225,1.352573362649574,1.2762965303130802,1.4296637040505331,0.6934480149551032,1.1871620540968337,1.0020486376596944,2.1783396939136637,0.9194814225041318,0.8539230863732928,1.2313944185682917,1.206784944340394,1.0921859497164579,1.4753445712895397,0.8981192770689264,0.5938465670347434,0.6783381931594962,1.030053084337561,0.8912012616546561,1.8776261211296028,1.0256757139046913,0.7949081484658429,11.005454894448038,1.1482968761677093,1.114266675122521,0.745959862342443,3.259291245874953,1.539236115370727,1.0658884099430677,1.181124874308747,0.6562099135805654,2.007825638728403,0.8216727608169174,1.070652070243264,1.4009316662865294,0.6326922950697935,0.8112266000346876,0.8252056001504723,1.0573241055394358,1.087639020847353,1.2411327691604213]
聚类中心：[0.2577915481907487,0.24187063799989547,0.24630985556481852,0.23957688913191325,0.25285488962258446,0.2335725992578756,0.22857939302669636,0.21827079940576968,0.24775928629066993,0.2218443249589329,0.331296594085653,0.29886418800641923,0.24276468516713678,0.26865854107410314,0.2101044981105112,0.2513680581594059,0.25173978753235926,0.2469653461196166,0.24972511616911328,0.25477414192273573,0.2729880069645572,0.25182046318638746,0.21951510211807507,0.22939417205092033,0.29968478515707364,0.2815389964005005,0.24557950724075683,0.2743620683619651,0.23752300497192522,0.22236597665714675,0.2694379372537791,0.227674354229177,0.2197397425131297,0.31808661922172904,0.25357582541296125,0.2455096790956047,0.24253560874692862,0.2628812992759077,0.20156411506800972,0.2756893375423326,0.24941635085430117,0.2732709296465288,0.26328450191286484,0.2327181637851255,0.2672763184856487,0.21823629739753156,0.2603888680768425,0.30482956606008305,0.2539800163703044,0.2494123872455821,0.2957145301691452,0.2574002272824942,0.2530279204146034,0.21638107292181635,0.22385306541179525,0.2656843824145815,0.24222258390631463,0.2737480467338501,0.2678335455867196,0.28650323216264373,0.27442498334750215,0.21603314170711163,0.22589211010635152,0.2721500040540577,0.25770521229453325,0.2502967844130666,0.24556852168319027,0.2863601023309159,0.289246549220357,0.3340719532653631,0.24272817673815253,0.2531820703185149,0.28207890657062273,0.3058354180517309,0.26993999677037445,0.37189590183442417,0.1835991775155036,0.2563337681534668,0.27472731107630777,0.2205331378439355,0.2916928995551411,0.2636916685440801,0.26998112980551814,0.25829062048720397,0.2075903046789032,0.24677293796360347,0.28416288416682123,0.22893344279592798,0.26421397688029524,0.2783961095933836,0.2010470028037374,0.25586457830095627,0.27300919895225984,0.3237450081760006,0.3524002657989988,0.31026933760091213,0.24554133063267086,0.280593472308155,0.26254276491682116,0.274124091414408]
聚类中心：[2.1936935571900107,1.9464118325346789,1.8127455375958357,12.856021834274635,3.196242979606894,1.8327203709576319,6.60923466607349,6.145588868343169,1.6304765773280192,2.4037561281537694,1.6218721582172888,1.9176090338660297,2.5563242485825657,2.5330776636145123,2.1761368851337455,2.0351703600725837,3.418287988622966,2.4882203291565053,4.7090289070762985,2.615375092955452,4.348355717801365,2.183213865012847,2.7460140997128923,2.1084925958613785,4.2133519700113675,1.4728077412253382,2.6930489913561377,1.586813422515331,3.064184237663189,2.567423520789301,1.3415123424671964,2.3337570299573485,2.9019216419970078,1.6024313945308815,2.2703382973715356,2.88243387058489,2.185906863994649,1.5211946226754227,2.165641985123635,3.296914774639655,2.3657616129738783,2.143822965872875,1.4970035109771866,2.7055707677328655,1.6132575897980483,3.9542260865140504,2.7890697045577935,1.6756054387521568,2.5999848962652448,1.5844996407451262,1.740164757878865,1.6274602177865818,2.15671407849582,2.2957344245267453,2.537250629465546,1.952085253028444,2.9341523451834983,3.3050071687865468,2.1570462592147925,1.7539532021426152,3.07171182779381,3.0403976091592977,3.705196437101558,1.4269930582887191,3.040126272667546,2.614644835562038,5.044014599662779,1.9463550956731372,1.7605885374307455,2.6809230067707532,3.017104955402999,2.4743113880697867,3.49565699569951,2.4251256677425994,1.4260543271362356,1.4191583090181965,2.4416074412501936,2.1887383779527236,4.137468621401845,2.8053247855932137,1.843002311878194,28.662906878946327,2.6634711053559568,2.2384824820312637,1.8492445533493866,9.72949090863246,4.159371998056375,2.316569863503567,3.003194418307522,1.3965769485727748,5.108385482353493,1.936578315850713,2.4890502080925234,3.749979479087212,1.3490516960487906,1.7064657811129953,1.7641649628908946,2.6384241548527574,3.115277541396776,3.1577298360812738]
聚类中心：[2.3861395213491665,1.3412103472088037,1.271067179940959,1.040451632258466,1.2038289764179546,2.9871551025952248,1.3798793938593181,0.9405472757770614,1.4650396777190282,1.5842678769966254,1.7646610371911724,2.2954157740341232,1.9662581932890422,1.622120545455118,1.3110318901980575,2.539136185868194,3.0090601596578277,1.3222024639851395,1.1875075004965305,6.467069852628031,1.3282730022891986,2.4336474001424375,1.5892529112307514,1.3372869452106644,2.4211682368136525,1.9221247678975983,1.72385375342219,1.8083476933944587,2.2850237810808913,1.1984349933631049,1.8655830227806902,1.7757890385470616,1.510689525080976,2.7438432313586456,1.3101363114149653,1.0172910475182326,2.4134114027501328,1.8144245028629085,1.2083783669112993,1.754845036560174,1.425998442065561,5.677199808152487,1.7244767215236836,1.4542896330675512,2.597620473034019,1.231135352324462,6.862600861164776,1.7801337878184127,1.1457916647001445,1.4253292527040493,1.8149153870040406,1.7045692009853057,2.187465809589689,1.3293011009479883,1.6226496386554567,2.7758916811124537,1.4779575032735128,1.7623821077140105,2.481283931055477,1.598505223018586,1.3397779640487666,1.310651739120562,1.2205922644569482,1.9964230937260912,2.0339057962213,1.6350312044622206,2.3745153683184324,1.9873426470356435,1.7002521567144415,2.050799777392442,1.5376843532200786,1.3246894466260573,1.6767143763562509,1.6562654049860397,1.416718321330371,2.635026980968954,1.0650918281996162,1.4374719495140302,1.4775556703776354,2.2700367396731487,2.412670057982965,1.236504220871974,1.8025773423900358,1.7512391376553766,1.9690563782562172,1.730727553076226,1.3651785876338747,1.7517448611843613,1.9047976972423453,1.9982374585128706,1.0312231305291015,1.336034164304123,1.9631068413640989,3.7954682072788364,1.5583639926827066,1.6483237916579452,1.8342663892711744,1.8873571119262957,1.6386050094361913,1.416429975705314]
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Within Set Sum of Squared Errors = 5.830304865438332E7你们在此过程中遇到了什么问题，欢迎留言，让我看看你们都遇到了哪些问题。