package DecesionTree
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext
import org.apache.spark.ml.feature.StringIndexer
import java.math._
import scala.collection.mutable.ArrayBuffer
/**
* 基於ID3算法選擇最優特徵---統計學習方法
*/
object ID3 {
def main(args: Array[String]): Unit = {
val conf=new SparkConf().setMaster("local").setAppName("ML")
val sc=new SparkContext(conf)
val sqlcontext=new SQLContext(sc)
import sqlcontext.implicits._
val sampleData=Array(Array("1","青年","否","否","一般","否"),Array("2","青年","否","否","好","否"),Array("3","青年","是","否","好","是"),
Array("4","青年","是","是","一般","是"),Array("5","青年","否","否","一般","否"),Array("6","中年","否","否","一般","否"),
Array("7","中年","否","否","好","否"),Array("8","中年","是","是","好","是"),Array("9","中年","否","是","非常好","是"),
Array("10","中年","否","是","非常好","是"),Array("11","老年","否","是","非常好","是"),Array("12","老年","否","是","好","是"),
Array("13","老年","是","否","好","是"),Array("14","老年","是","否","非常好","是"),Array("15","老年","否","否","一般","否"))
val newData=sc.parallelize(sampleData).map { x =>
val age=x(1)
val work=x(2)
val house=x(3)
val credit=x(4)
val label=x(5)
(age,work,house,credit,label)
}.persist(StorageLevel.MEMORY_ONLY_SER)
val DF=newData.toDF("age","isWork","isHouse","credit","label").persist(StorageLevel.MEMORY_ONLY_SER)
val features=DF.columns
//計算數據集D的熵
val totalRecord=newData.count()
val labels=newData.map(x=>(x._5,1)).reduceByKey(_+_).collect()
var Hd=0.0
for(lab<-labels){
val labelcount=lab._2.toDouble
val pi=labelcount/totalRecord
Hd+= -1.0*((pi)*Math.log(pi)/Math.log(2))
}
//計算特徵A對數據集的經驗條件熵
val Hda=ArrayBuffer[Double]()
for(feature<-features){
var Hdik=0.0
if(!"label".equals(feature)){
//DI表示特徵A對應的信息
val DI=DF.groupBy(feature).count()
//lab表示特徵A所有可能得取值
val Lab=ArrayBuffer[String]()
val Di=ArrayBuffer[Int]()
DI.collect().map { Row =>
Lab +=Row.getString(Row.fieldIndex(feature))
Di +=Row.getLong(Row.fieldIndex("count")).toInt
}
//獲取Dik信息
val Dik=ArrayBuffer[(Int,Int)]()
for(lab<-Lab){
var i=0
val str=s"$feature = " + s"'$lab'"
println("str:"+str)
val newDF=DF.where(str).groupBy("label").count()
val df=newDF.rdd.map { Row => Row.getLong(Row.fieldIndex("count")).toInt}.collect()
if(newDF.count().toInt ==2) Dik.append((df(0),df(1))) else Dik.append((df(0),0))
}
//計算每個label的條件熵
for(i<-Di){
val newDik=Dik.take(1)
Dik.remove(0,1)
for(j<-newDik){
if(j._2 ==0){
val pi=j._1.toDouble/i
Hdik += i.toDouble/totalRecord*(-1.0)*(pi)*Math.log(pi)/Math.log(2)
}else{
val pi1=j._1.toDouble/i
val pi2=j._2.toDouble/i
Hdik += i.toDouble/totalRecord*(-1.0)*(pi1)*Math.log(pi1)/Math.log(2) + i.toDouble/totalRecord*(-1.0)*(pi2)*Math.log(pi2)/Math.log(2)
}
}
}
Hda.append(Hdik)
}
}
//Gda表示信息增益,選取信息增益最大值作爲最優特徵。
val Gda=ArrayBuffer[(String,Double)]()
for(i<-0 until Hda.length){
val hda=Hda(i)
Gda.append((features(i),(Hd-hda)))
}
Gda.foreach { x => println(x) }
}
}
實驗結果:
(age,0.08300749985576883)
(isWork,0.3236501981515564)
(isHouse,0.419973094021975)
(credit,0.3629895625370855)
算法小白的第一測嘗試---ID3(Decision Tree)
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