lakala proportion轨迹分析代码

/**
  * Created by lkl on 2017/12/7.
  */
import breeze.numerics.abs
import org.apache.spark.sql.SQLContext
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
object proportion {

  def main(args: Array[String]): Unit = {
    val cf = new SparkConf().setAppName("ass").setMaster("local")
    val sc = new SparkContext(cf)
    val sqlContext = new SQLContext(sc)

    val File1 = sc.textFile("20171117PP.txt").filter(_.contains("OK")).map(_.replace(",0],","a[").split("a").last).map(_.replace("OK", "")).map(_.replace("FAIL", "")).map(line => (line.split(";").last.toDouble, line.split(";").head))
    File1.saveAsTextFile("")
    val File2=sc.textFile("20171117PP.txt").filter(_.contains("FAIL")).map(_.replace(",0],","a[").split("a").last).map(_.replace("OK", "")).map(_.replace("FAIL", "")).map(line => (line.split(";").last.toDouble, line.split(";").head))
    val b=File2.randomSplit(Array(0.1, 0.9))
    val (strainingDatas, stestDatas) = (b(), b())
    val File=File1 union(strainingDatas)
    val ass = File.map { p => {
      var str = ""
      val l = p._1
      val a = p._2.substring(, p._2.length - )
      val b = a.replace("],[", "a")
      val c = b.split("a")
      for (arr <- c) {
        val index1 = arr.split(",")().toInt + ","
        val index2 = arr.split(",")().toInt + ","
        val index3 = arr.split(",")().toInt + " "
        val index = index1 + index2 + index3
        str += index
      }
      (l, str.substring(, str.length - ))
    }
    }

    val rdd = ass.map( p => {
      val l=p._1
      val rowall =new Array[Double]()
      val arr = p._2.split(" ")
      var map:Map[Int,List[Double]] = Map()
      var vlist:List[Double] = List()
      for(a <- arr){
        val x = a.split(",")().toInt
        val y =  a.split(",")().toInt+
        val t = a.split(",")().toInt
        val index = (x*)+(y+)
        val v = t
        vlist = v :: map.get(index).getOrElse(List())
        map += (index -> vlist)

      }
      map.foreach(p => {
        val k = p._1
        val v = p._2
        val sv = v.toSet.size
        val rv = sv.toDouble/v.size.toDouble
        val tmp =f"$rv%1.2f".toDouble
        rowall(k) = tmp
      })
      (l,rowall)
    })

    val usersList = rdd.map(p => {
      LabeledPoint(p._1.toDouble, Vectors.dense(p._2.toArray))
    })

    val splits = usersList.randomSplit(Array(, ))
    val (trainingData, testData) = (splits(), splits())
    // 随机森林训练参数设置
    //分类数
    val numClasses =
    // categoricalFeaturesInfo 为空，意味着所有的特征为连续型变量
    val categoricalFeaturesInfo = Map[Int, Int]()
    //树的个数
    val numTrees =
    //特征子集采样策略，auto 表示算法自主选取
    val featureSubsetStrategy = "auto"
    //纯度计算
    val impurity = "gini"
    //树的最大层次
    val maxDepth =
    //特征最大装箱数
    val maxBins =
    //训练随机森林分类器，trainClassifier 返回的是 RandomForestModel 对象
    val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
      numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)

    val labelAndPreds = testData.map { point =>
      val prediction = model.predict(point.features)
      (point.label, prediction)
    }

//    import sqlContext.implicits._
//    val ss = labelAndPreds.toDF("a", "b").registerTempTable("people")
//    val teenagers = sqlContext.sql("SELECT count(1) FROM people WHERE a-b=0")
//    teenagers.collect()
    val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
    val accurate =  - testErr.toDouble
    println("Test Error = " + accurate)
    println("Learned classification forest model:\n" + model.toDebugString)

    // 将训练后的随机森林模型持久化
    model.save(sc, "myModelPath23")
    //加载随机森林模型到内存
    val sameModel = RandomForestModel.load(sc, "myModelPath")

  }

  }