选取了10个文档,其中4个来自于一篇论文,3篇来自于一篇新闻,3篇来自于另一篇新闻。
首先在pom文件中加入mysql-connector-java:
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.6</version>
</dependency>
main函数:
def main(args:Array[String]): Unit ={
// 连接MySQL数据库
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() // 记录属性
val prop = new java.util.Properties
prop.setProperty("user", "hadoop")
prop.setProperty("password", "root")
prop.setProperty("driver", "com.mysql.jdbc.Driver") // 读取文档的单词到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() // 从Vocabulary表中删除StopWord表里面的词
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()
// 主题数量
.setK(3)
// 超参数(伪计数)
.setDocConcentration(1.1)
// 超参数(伪计数)
.setTopicConcentration(1.1)
// 最大迭代次数
.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):")
// 主题分布
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)
} // 主题分布排序
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)
} // 文档分布
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
}
统计每个文档的每个单词出现的次数:
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 => {
// 对word进行处理,包括去除符号,转换成小写等
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
}
从Vocabulary表中删除StopWord表里面的词:
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!")
}
把停用词插入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) // 写入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 => {
// 该单词写入表vocabulary
ps = conn.prepareStatement(sql)
ps.setString(1, p._1)
ps.executeUpdate()
})
println("Stopword finishes inserting!") }
把文档的单词插入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 => {
// 对word进行处理,包括去除符号,转换成小写等
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) // 写入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)){
// 已经存在这个单词则不写入
isWrite = false
break
}
}
)
}
// 该单词写入表vocabulary
if (isWrite){
ps = conn.prepareStatement(sql)
ps.setString(1, p._1)
ps.executeUpdate()
}
})
println(path + " finishes inserting!")
}
运行结果:
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) 193(2.5145205580836416E-5) 194(8.088137944312175E-5) 195(7.737822026514119E-5) 196(2.5145205580836416E-5) 197(2.5145205580836416E-5) 198(0.9999514499218066) 199(2.5145205580836416E-5) 200(2.5145205580836416E-5) 201(5.546734543071504E-5) 202(2.5145205580836416E-5) 203(2.5145205580836416E-5) 204(2.7206965341773027E-5) 205(2.5145205580836416E-5) 206(2.5145205580836416E-5) 207(2.5145205580836416E-5) 208(2.735649722677541E-5) 209(2.5145205580836416E-5) 210(2.746431548406046E-5) 211(0.9998741344447578) 212(5.0989275150431786E-5) 213(2.5145205580836416E-5) 214(2.7118027897493872E-5) 215(0.9991736181823824) 216(2.5145205580836416E-5) 217(2.5145205580836416E-5) 218(2.5145205580836416E-5) 219(0.9986359452254359) 220(2.5145205580836416E-5) 221(2.5145205580836416E-5) 222(2.5145205580836416E-5) 223(0.9992607891973315) 224(4.936699969890295E-5) 225(2.5145205580836416E-5) 226(3.979229408939385E-5) 227(2.5145205580836416E-5) 228(0.9965306937976125) 229(2.7118027897493872E-5) 230(2.676801478412973E-5) 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1249(4.5122508499873454E-5) 1250(4.307037755864134E-5) 1251(4.307037755864134E-5) 1252(4.307037755864134E-5) 1253(0.99906235393172) 1254(4.307037755864134E-5) 1255(4.307037755864134E-5) 1256(4.307037755864134E-5) 1257(4.307037755864134E-5) 1258(4.307037755864134E-5) 1259(4.307037755864134E-5) 1260(4.679458055842222E-5) 1261(4.307037755864134E-5) 1262(4.6223633387041405E-5) 1263(0.99906235393172) 1264(4.307037755864134E-5) 1265(4.307037755864134E-5) 1266(4.307037755864134E-5) 1267(4.307037755864134E-5) 1268(4.307037755864134E-5) 1269(4.307037755864134E-5) 1270(0.99906235393172) 1271(4.307037755864134E-5) 1272(0.999769651168907) 1273(0.999769651168907) 1274(1.9997586652601287) 1275(0.999769651168907) 1276(0.999769651168907) 1277(0.999769651168907) 1278(0.999769651168907) 1279(0.999769651168907) 1280(0.999769651168907) 1281(1.9997586652601287) 1282(0.999769651168907) 1283(2.9997547672248634) 1284(0.999769651168907) 1285(0.999769651168907) 1286(0.999769651168907) 1287(0.999769651168907) 1288(0.999769651168907) 1289(0.999769651168907) 1290(0.999769651168907) 1291(2.9997547672248634) 1292(0.999769651168907) 1293(0.999769651168907) 1294(8.999749370150024) 1295(0.999769651168907) 1296(0.999769651168907) 1297(0.999769651168907) 1298(0.999769651168907) 1299(0.999769651168907) 1300(0.999769651168907) 1301(0.999769651168907) 1302(0.999769651168907) 1303(0.999769651168907) 1304(5.999750741513635) 1305(0.999769651168907) 1306(0.999769651168907) 1307(0.999769651168907) 1308(0.999769651168907) 1309(0.999769651168907) 1310(0.999769651168907) 1311(0.999769651168907) 1312(0.999769651168907) 1313(0.999769651168907) 1314(0.999769651168907) 1315(1.9997586652601287) 1316(0.999769651168907) 1317(0.999769651168907) 1318(0.999769651168907) 1319(0.999769651168907) 1320(0.999769651168907) 1321(6.99975015562379) 1322(1.9997586652601287) 1323(1.9997586652601287)
The topics described by their top-weighted terms:
Topic 0:0(0.026537654308753947) 1(0.022291595584459593) 2(0.01805376454632414) 3(0.016994449901753302) 4(0.012748901211959393) 5(0.012740514417610937) 6(0.012738038629205008) 7(0.01273801561924325) 8(0.011676529759440337) 9(0.010614979074646718)
Topic 1:0(0.014607438152180853) 1(0.014598111550937454) 2(0.013559476460060267) 3(0.013555391429087675) 4(0.011473295929068187) 5(0.010427262939480266) 6(0.010427262939480266) 7(0.010427185065023511) 8(0.00938853183624946) 9(0.009385227376273907)
Topic 2:0(0.013807670229605759) 1(0.012821404351493447) 2(0.011834519149445158) 3(0.010848808634841414) 4(0.010848808634841414) 5(0.009862562012887958) 6(0.009862542832894608) 7(0.008877277286893682) 8(0.008876340901933325) 9(0.008876145972864815)
The documentation distribution:
0 [7.333146435036974E-5,6.080960819974437E-5,0.99986585892745]
1 [2.6663949298565034E-4,0.999649289033629,8.407147338540428E-5]
2 [0.9998175992795761,1.1652366091318298E-4,6.587705951070862E-5]
3 [1.4469275471657022E-4,0.9996555194522557,1.997877930276974E-4]
4 [0.9996659842219435,1.942152940732567E-4,1.3980048398321528E-4]
5 [6.57922549992311E-5,0.9998590561541768,7.515159082395228E-5]
6 [1.5473658550027247E-4,2.315996735072716E-4,0.9996136637409924]
7 [2.3259757591235534E-4,1.851791507569187E-4,0.9995822232733307]
8 [0.9998317528382852,3.189857050859758E-5,1.363485912062577E-4]
9 [2.909973281987435E-4,2.4861458428830874E-4,0.999460388087513]