spark发现新词

package com.icklick.spark.wordSegment
import org.apache.log4j.{ Level, Logger }
import org.apache.spark.{ SparkConf, SparkContext }
import com.iclick.spark.wordSegment.util.CounterMap
import scala.collection.mutable.ArrayBuffer
import com.google.common.collect.Maps
import java.text.SimpleDateFormat
import scala.collection.JavaConversions._
import scala.collection.JavaConverters._
import scala.collection.mutable.Map
import com.iclick.spark.wordSegment.util.AtomsUitl
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SaveMode
import com.iclick.spark.wordSegment.util.ConterHashSet
import org.apache.commons.lang.StringUtils
import com.mysql.jdbc.Driver
///tmp/yuming/webtable/ds=16-04-17 hadoop数据目录
object WordSegment{
def main(args: Array[String]): Unit = {
//关闭一些不必要的日志
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
//master
if (args.length < 5) {
System.err.println("Usage: path ,maxLen ,pmi, info,shuffle_count")
System.exit(1)
}
val path=args(0).toString
val maxLen=args(1).toInt
val pmi=args(2).toDouble
val info=args(3).toDouble
val shuffle_count=args(4).toInt
val save_path_result=if(args.length>=6){ args(5).toString} else "/tmp/wilson/"
val conf = new SparkConf().set("spark.driver.maxResultSize","10g").
set("spark.sql.shuffle.partitions",s"${shuffle_count}").set("spark.network.timeout","850s").
set("spark.shuffle.compress","true").set("spark.shuffle.spill.compress","true").set("spark.shuffle.manager","sort")
if (System.getProperty("local") != null) {
conf.setMaster("local").setAppName("wordSegname")
}
val sc = new SparkContext(conf)
val sqlContext=new SQLContext(sc)
//local
/* val conf = new SparkConf().setAppName("wordSegname").setMaster("local[4]").
set("spark.sql.shuffle.partitions","10").set("spark.network.timeout","30s")
.set("spark.shuffle.compress","true").set("spark.shuffle.spill.compress","true")
.set("spark.shuffle.manager","sort")
val sc = new SparkContext(conf)
val sqlContext=new SQLContext(sc)
val path="D:\\wilson.zhou\\Downloads\\西游记.txt"
val maxLen=6
val path1="D:\\temp\\text.txt"
val pmi=0
val info=0
val save_path_result="/tmp/wilson/"*/
// val word=scala.io.Source.fromFile("D:\\wilson.zhou\\Downloads\\红楼梦.txt").getLines().mkString("")
val sdf = new java.text.SimpleDateFormat("yyyy-MM-dd:HH:mm:ss")
var start=sdf.format(System.currentTimeMillis())
val word1=sc.textFile(path).map{x=>
val x_filter=x.replaceAll("[" + AtomsUitl.stopwords + "]", " ").replaceAll("\\p{Punct}", " ").replaceAll("\\pP", " ")
.replaceAll("　", " ").replaceAll("\\p{Blank}", " ").replaceAll("\\p{Space}", " ").replaceAll("\\p{Cntrl}", " ")
x_filter
}
val sum_document=word1.count()
val word_document=word1.zipWithIndex.filter { x => !StringUtils.isBlank(x._1) }.flatMap{x=>
val arr= ArrayBuffer[(String,Int)]()
val line=x._1.split(" ")
for(i<-line){
arr+=((i,x._2.toInt))
}
arr }.map{x=>(x._1.trim,x._2)}.filter(x=> !StringUtils.isBlank(x._1))
println("Calculate the iterms documnt")
val word_document_caculate= word_document.map{x=>("$"+ x._1 +"$",x._2)}.flatMap{
x=> var arr=ArrayBuffer[(String,Int)]()
for( y<- 1 to AtomsUitl.len(x._1)-2){
arr+=((AtomsUitl.substring(x._1,y, Math.min(maxLen+y,AtomsUitl.len(x._1))),x._2))
}
arr
}.sortBy(x=>x._1)
println("documnet caculate will start")
val word_document_result=word_document_caculate.map{
x=>
val first=AtomsUitl.substring(x._1, 0, 1)
(first,x._1,x._2)
}.groupBy((f:(String,String,Int))=>f._1).map{
x=>x._2
}.flatMap{
x=>
val documnet=Maps.newHashMap[String,ConterHashSet]
var arrBuff=ArrayBuffer[(String,Int)]()
for(curr <- x){
for( ii<- 1 to AtomsUitl.len(curr._2)-1){
val w1=AtomsUitl.substring(curr._2, 0,ii)
if(documnet.containsKey(w1)){
documnet.get(w1).addelment(curr._3.asInstanceOf[java.lang.Integer])
}else{
val cm=new ConterHashSet();
cm.addelment(curr._3.asInstanceOf[java.lang.Integer])
documnet.put(w1,cm)
}
}
}
val documnet_iter=documnet.keySet.iterator
while(documnet_iter.hasNext()){
val w=documnet_iter.next()
val freq=documnet.get(w).getsize()
arrBuff+=((w,freq))
}
arrBuff
}
// word_document_result.take(20).foreach(println)
// println("word_document_result's count："+word_document_result.count())
println("information entropy and information")
val word=word1.flatMap{x=>
val line=x.split(" ")
line
}.filter(x=> !StringUtils.isBlank(x))
// //计算左信息熵做准备
println("Calculate the left word information entropy and information entropy .....")
val wordleft=word.map(x=>AtomsUitl.reverse(x)).map{x=>"$"+ x +"$"}.flatMap{
x=> var arr=ArrayBuffer[String]()
for( y<- 1 to AtomsUitl.len(x)-2){
// arr+=x.substring(y, Math.min(maxLen + y, x.length()))
arr+=AtomsUitl.substring(x,y, Math.min(maxLen + y, AtomsUitl.len(x)))
}
arr
}.sortBy(x=>x)
val wordleft_caculate= wordleft.map{
s=>
// val first=s.substring(0, 1).toString()
val first=AtomsUitl.substring(s, 0,1).toString
(first,s)
}.groupBy((f:(String,String))=>f._1).map{
x=>x._2
}.flatMap{
x=>
val stat = Maps.newHashMap[String, CounterMap]()
var arrBuff=ArrayBuffer[(String,Double)]()
for(curr <- x){
for( ii<- 1 to AtomsUitl.len(curr._2)-1){
// val w = curr._2.substring(0,ii)
val w = AtomsUitl.substring(curr._2, 0, ii)
// val suffix = curr._2.substring(ii).substring(0, 1)
val suffix= AtomsUitl.substring(AtomsUitl.substring(curr._2,ii),0,1)
if (stat.containsKey(w)) {
stat.get(w).incr(suffix)
} else {
val cm = new CounterMap()
cm.incr(suffix)
stat.put(w, cm)
}
}
}
var iterator_stat=stat.keySet().iterator()
while(iterator_stat.hasNext()){
var w=iterator_stat.next()
var cm = stat.get(w);
var freq = 0
var re = 0.0
var cm_iter=cm.countAll().keySet().iterator()
while(cm_iter.hasNext()) {
freq += cm.get(cm_iter.next())
}
var cm_iter1=cm.countAll().keySet().iterator()
while(cm_iter1.hasNext()) {
var p = cm.get(cm_iter1.next()) * 1.0 / freq
re += -1 * Math.log(p) * p
}
// print("freq的值是："+freq+" ")
// println("re的值是："+re)
arrBuff+=((AtomsUitl.reverse(w),re))
}
arrBuff
}
// wordleft_caculate.take(20).foreach(println)
// println("左邻信息个个数是："+wordleft_caculate.count())
// println(wordleft_caculate.map(x=>x._1).distinct().count())
// println("wordleft'coutn----->"+wordleft.count)
//计算右信息熵做准备
println("Calculate the right word information entropy and information entropy .....")
val wordright=word.map{x=>"$"+ x +"$"}.flatMap{
x=>
var arr=ArrayBuffer[String]()
// AtomsUitl.len(x)-2
for( y<- 1 to AtomsUitl.len(x)-2){
// arr+=x.substring(y, java.lang.Math.min(maxLen + y, x.length()))
arr+=(AtomsUitl.substring(x,y,Math.min(maxLen+y,AtomsUitl.len(x))))
}
arr
}.sortBy(x=>x)
//计算右邻字信息熵
val wordright_caculate=wordright.map{
s=>
// val first=s.substring(0, 1).toString()
val first=AtomsUitl.substring(s, 0,1).toString()
(first,s)
}.groupBy((f:(String,String))=>f._1).map{
x=>x._2
}.flatMap{
x=>
var stat = Maps.newHashMap[String, CounterMap]()
var arrBuff=ArrayBuffer[(String,Int,Double)]()
for(curr <- x){
for(i<- 1 to AtomsUitl.len(curr._2)-1){
// val w = curr._2.substring(0, i)
val w=AtomsUitl.substring(curr._2,0,i)
// val suffix = curr._2.substring(i).substring(0, 1)
val suffix=AtomsUitl.substring(AtomsUitl.substring(curr._2, i), 0,1).toString
if (stat.containsKey(w)) {
stat.get(w).incr(suffix);
} else {
val cm = new CounterMap();
cm.incr(suffix);
stat.put(w, cm);
}
}
}
var iterator_stat=stat.keySet().iterator()
while(iterator_stat.hasNext()){
var w=iterator_stat.next()
var cm = stat.get(w);
var freq = 0
var re = 0.0
var cm_iter=cm.countAll().keySet().iterator()
while(cm_iter.hasNext()) {
freq += cm.get(cm_iter.next())
}
var cm_iter1=cm.countAll().keySet().iterator()
while(cm_iter1.hasNext()) {
var p = cm.get(cm_iter1.next()) * 1.0 / freq
re += -1 * Math.log(p) * p
}
// print("w的值是："+w+" ")
// print("freq的值是："+freq+" ")
// println("re的值是"+re)
arrBuff+=((w,freq,re))
}
arrBuff
}
// println("计算右邻信息前20条")
// wordright_caculate.take(20).foreach(println)
// println("右信息表的总共个数："+wordright_caculate.count())
// wordright_caculate.
//左右合并开始
println(" Merge will begin to calculated..............")
import sqlContext.implicits._
/* val word_caculate_total1=wordright_caculate.union(wordleft_caculate).sortBy(x=>x).groupBy((f:(String,Int,Double))=>f._1,20).map(x=>x._2)
val word_caculate_total= word_caculate_total1.map{
x=>
val hashtable=new java.util.Hashtable[String,String]()
hashtable.put("name","null")
hashtable.put("freq","0")
hashtable.put("e",java.lang.Double.MAX_VALUE.toString())
for(str<-x){
hashtable.put("name",str._1)
if(str._2!= -20){
hashtable.put("freq",String.valueOf(str._2))
}
if(str._3<java.lang.Double.parseDouble(hashtable.get("e"))){
hashtable.put("e",String.valueOf(str._3))
}
}
(hashtable.get("name") ,hashtable.get("freq").toInt,hashtable.get("e").toDouble)
}.filter(x=> !StringUtils.isBlank(x._1) && x._1.length>1)*/
val wordright_caculate_todf= wordright_caculate.toDF("right_name","freq","right_info")
val wordleft_caculate_todf= wordleft_caculate.toDF("left_name","left_info")
val udf_get_min:((Double,Double)=>Double)=(arg1:Double,arg2:Double)=>Math.min(arg1,arg2)
val sqlfunctin=udf(udf_get_min)
val word_caculate_total=wordright_caculate_todf.join(wordleft_caculate_todf,wordright_caculate_todf("right_name")===wordleft_caculate_todf("left_name"),"left").
withColumn("info", sqlfunctin(col("right_info"),col("left_info"))).drop("right_info").
drop("left_name").drop("left_info").filter(length(wordright_caculate_todf("right_name"))>1).rdd
// wordright_caculate.union(wordleft_caculate).groupBy((f:(String,Int,Double))=>f._1).map(x=>x._2).take(20).foreach(println)
println("计算凝固度")
val size_pmi=wordright_caculate.count()
println("最后步骤中的size的总数是："+size_pmi)
println("map_total has down")
//计算凝固度
val last= word_caculate_total.flatMap{
x=>
var w=x.apply(0).toString
var f=x.apply(1).toString.toInt
var e=x.apply(2).toString.toDouble
// var w=x._1
// var f=x._2
// var e=x._3
var arr=ArrayBuffer[(String,Int,Double,String,String)]()
for(s <- 1 to AtomsUitl.len(w)-1){
// var lw=w.substring(0,s)
try{
var lw=AtomsUitl.substring(w, 0,s)
// var rw=w.substring(s)
var rw=AtomsUitl.substring(w, s)
arr+=((w,f,e,lw,rw))
}catch{
case e:Exception=>arr+=(("",0,0.0,"",""))
}
}
arr
}.filter(f=> !StringUtils.isBlank(f._4)&& !StringUtils.isBlank(f._5))
println("dataframe merge will begin to calculated..............")
// last.take(30).foreach(println)
val df= last.toDF("w_total","f","e","lw","rw")
val df1=wordright_caculate.toDF("w","freq","re")
val df2_drop=df.join(df1,df("lw")===df1("w"),"left").drop("re").drop("w").withColumnRenamed("freq", "lw_freq")
// val df2_drop=df2.drop("re").drop("w").withColumnRenamed("freq", "lw_freq")
val df3_drop=df2_drop.join(df1,df2_drop("rw")===df1("w"),"left").drop("re").drop("w").withColumnRenamed("freq", "rw_freq")
// val df3_drop=df3.drop("re").drop("w").withColumnRenamed("freq", "rw_freq")
// 948014
//凝固度計算
/*val result=df3_drop.rdd.groupBy{f=>f(0)}.map{
x=>
val map=new java.util.HashMap[String,String]()
map.put("max","1")
for(i<-x._2){
map.put("w_total",i.apply(0).toString)
map.put("f",i.apply(1).toString)
map.put("e",i.apply(2).toString)
var ff:java.lang.Long=try{
i.apply(5).toString.toLong*i.apply(6).toString.toLong
}catch{
case e:Exception=>1l
}
if(ff>map.get("max").toLong){
map.put("max",ff.toString)
}
}
var pf=map.get("f").toLong*size_pmi*1.0/map.get("max").toLong
var pmi=Math.log(pf)
var w_total= map.get("w_total")
var f=map.get("f").toInt
var e=map.get("e").toDouble
map.clear()
(w_total,f,pmi,e,0)
// ( map.get("w_total"),map.get("f").toInt ,pmi,map.get("e").toDouble,0)
}.filter(f=>f._3>pmi&& f._4>info&& !StringUtils.isBlank(f._1))
val resultToDf= result.toDF("name","freq","pmi","info","zero")
*/
println("dataframe join has down")
//计算凝聚度改用DataFrame的形式
val udf_get_pmi=(arg1:Int,arg2:Int,arg3:Int)=>Math.log((arg1.toLong*size_pmi.toLong*1.0)/(arg2.toLong*arg3.toLong))
val udf_get_pmi_udf=udf(udf_get_pmi)
val resultToDf=df3_drop.withColumn("pmi",udf_get_pmi_udf(col("f"),col("rw_freq"),col("lw_freq"))).withColumn("zero", col("f")*0).
drop("rw_freq").drop("lw_freq").drop("lw").drop("rw").sort($"w_total",$"pmi".desc).dropDuplicates(Array("w_total")).
filter($"pmi">pmi && $"e">info).withColumnRenamed("w_total", "name").withColumnRenamed("f", "freq").withColumnRenamed("e", "info")
println("The final result will be caculated")
val word_document_resultToDf=word_document_result.toDF("name1","document")
val resultToDf2= resultToDf.join(word_document_resultToDf,word_document_resultToDf("name1")===resultToDf("name"),"left").
withColumn("documentcount",col("zero")+sum_document).drop("zero").drop("name1")
// val resultToDf2 =resultToDf1.withColumn("documentcount",col("zero")+sum_document).drop("zero").drop("name1")
// resultToDf2.show(20)
// 互信息凝聚度pmi
// 左右熵 e
//把结果存入到hdfs中
println("Results will stored into HDFS.")
val sdf1=new SimpleDateFormat("yy-MM-dd")
val save_path=save_path_result+sdf1.format(System.currentTimeMillis())
try{
resultToDf2.rdd.map{
x=>
var name=x.apply(0).toString
var freq=x.apply(1).toString
var entropy=x.apply(2).toString
var info=x.apply(3).toString
var document=x.apply(4).toString
var documenttotal=x.apply(5).toString
s"${name},${freq},${info},${entropy},${document},${documenttotal}"
}.saveAsTextFile(save_path)
println("....................sucess.............")
// resultToDf2.rdd.repartition(1).saveAsTextFile(save_path)
}catch{
case e:Exception=>println("some errors happend when sava the last datas")
}
//把结果插入到mysql数据库中
/* val driver="com.mysql.jdbc.Driver"
Class.forName(driver)
val url ="jdbc:mysql://10.1.1.28:3306/spark"
val pro=new java.util.Properties
pro.setProperty("user","usr_dba")
pro.setProperty("password","4rfv%TGB^YHN")
pro.setProperty("use_unicode", "true")
pro.setProperty("characterEncoding", "utf8")
resultToDf2.write.mode(SaveMode.Overwrite).jdbc(url, "wordsegment",pro)
*/
println(start)
println(sdf.format(System.currentTimeMillis()))
sc.stop()
}
}

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