[大数据]spark高级数据分析实战---随机森林实现

**package** mllib.tree

**import** org.apache.log4j.{Level, Logger}
**import** org.apache.spark.mllib.evaluation.MulticlassMetrics
**import** org.apache.spark.mllib.linalg.Vectors
**import** org.apache.spark.mllib.regression.LabeledPoint
**import** org.apache.spark.mllib.tree.{RandomForest, DecisionTree}
**import** org.apache.spark.mllib.tree.model.{RandomForestModel, DecisionTreeModel}
**import** org.apache.spark.rdd.RDD
**import** org.apache.spark.{SparkContext, SparkConf}


_/**_ _* Created by_ _汪本成_ _on 2016/7/18._ _*/_ **object** randomForest {
  //屏蔽不必要的日志显示在终端上
//  Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
//  Logger.getLogger("org.apache.eclipse.jetty.server").setLevel(Level.OFF)

  **var** _beg_ = System.currentTimeMillis()
  //创建入口对象
  **val** _conf_ = **new** SparkConf().setAppName("rndomForest").setMaster("local")
  **val** _sc_ = **new** SparkContext( _conf_ )

  **val** _HDFS_COVDATA_PATH_ = "hdfs://192.168.43.150:9000/user/spark/sparkLearning/mllib/covtype.data"
  **val** _rawData_ = _sc_.textFile( _HDFS_COVDATA_PATH_ )

  //设置LabeledPoint格式
  **val** _data_ = _rawData_.map{
    line =>
      **val** values = line.split(",").map(_.toDouble)
      // init返回除最后一个值之外的所有值，最后一列是目标
      **val** FeatureVector = Vectors.dense(values.init)
      //决策树要求(目标变量)label从0开始，所以要减一
      **val** label = values.last - 1
      LabeledPoint(label, FeatureVector)
  }

  //分成训练集(80%)，交叉验证集(10%)，测试集(10%)
  **val** Array( _trainData_ , _cvData_ , _testData_ ) = _data_.randomSplit(Array(0.8, 0.1, 0.1))
  _trainData_.cache()
  _cvData_.cache()
  _testData_.cache()

  //新建随机森林
  **val** _numClass_ = 7  //分类数量
  **val** _categoricalFeaturesInfo_ = _Map_ [Int, Int](10 -> 4, 11-> 40)  //用map存储类别(离散)特征及每个类特征对应值(类别)的数量
  **val** _impurity_ = "entropy"  //纯度计算方法，用于信息增益的计算
  **val** _number_ = 20  //构建树的数量
  **val** _maxDepth_ = 4  //树的最大高度
  **val** _maxBins_ = 100   // 用于分裂特征的最大划分数量

  //训练分类决策树模型
  **val** _model_ = RandomForest.trainClassifier( _trainData_ , _numClass_ , _categoricalFeaturesInfo_ , _number_ , "auto", _impurity_ , _maxDepth_ , _maxBins_ )

  **val** _metrics_ = getMetrics( _model_ , _cvData_ )
  //计算精确度(样本比例)
  **val** _precision_ = _metrics_. _precision_ __ //计算每个样本的准确度(召回率)
  **val** _recall_ = (0 until 7).map(     //DecisionTreeModel模型的类别号从0开始
    cat => ( _metrics_.precision(cat), _metrics_.recall(cat))
  )

  **val** _end_ = System.currentTimeMillis()
  //耗时时间
  **var** _castTime_ = _end_ - _beg_ ____**def** main(args: Array[String]) {
    println("========================================================================================")
    //精确度(样本比例)
    println("精确度: " + _precision_ )
    println("========================================================================================")
    //准确度(召回率)
    println("准确度: ")
    _recall_.foreach(println)
    println("========================================================================================")
    println(" 运行程序耗时: " + _castTime_ /1000 + "s")

  }

  _/**_ _*_ _在训练集构建RandomForestModel_ ___*_ ** _@param model_** ** __**_*_ ** _@param data_** ** __**_*_ ** _@return_** ** __**_*/_ __**def** getMetrics(model: RandomForestModel, data: RDD[LabeledPoint]): MulticlassMetrics = {
    **val** predictionsAndLabels = data.map(example => (model.predict(example.features), example.label))
    **new** MulticlassMetrics(predictionsAndLabels)
  }
  _/**_ _*_ _按照类别在训练集出现的比例预测类别_ ___*_ _*_ ** _@param data_** ** __**_*_ ** _@return_** ** __**_*/_ __**def** classProbabilities(data: RDD[LabeledPoint]): Array[Double] = {
    //计算数据中每个类别的样本数（类别， 样本数）
    **val** countsByCategory = data.map(_.label).countByValue()
    //对类别的样本数进行排序并取出样本数
    **val** counts = countsByCategory.toArray.sortBy(_._1).map(_._2)
    counts.map(_.toDouble / counts.sum)
  }

}

[/code]

运行结果如下

16/07/18 23:30:11 INFO DAGScheduler: ResultStage 17 (collectAsMap at
MulticlassMetrics.scala:54) finished in 0.003 s
16/07/18 23:30:11 INFO TaskSchedulerImpl: Removed TaskSet 17.0, whose tasks
have all completed, from pool
16/07/18 23:30:11 INFO DAGScheduler: Job 9 finished: collectAsMap at
MulticlassMetrics.scala:54, took 0.197033 s

精确度: 0.5307208847065288

准确度:
(0.8087885985748219,0.03206818609907704)
(0.5233824352041768,0.9884494841004331)
(0.5730994152046783,0.6121521862578081)
(0.0,0.0)
(0.0,0.0)
(0.0,0.0)
(0.0,0.0)

运行程序耗时: 44s
16/07/18 23:30:12 INFO SparkContext: Invoking stop() from shutdown hook
16/07/18 23:30:12 INFO SparkUI: Stopped Spark web UI at
http://192.168.43.1:4040
16/07/18 23:30:12 INFO MapOutputTrackerMasterEndpoint:
MapOutputTrackerMasterEndpoint stopped!
16/07/18 23:30:12 INFO MemoryStore: MemoryStore cleared
16/07/18 23:30:12 INFO BlockManager: BlockManager stopped
16/07/18 23:30:12 INFO BlockManagerMaster: BlockManagerMaster stopped
16/07/18 23:30:12 INFO
OutputCommitCoordinatorKaTeX parse error: Undefined control sequence: \Users at position 274: …ng directory C:\?U?s?e?r?s?\Administrator\…RemotingTerminator: Shutting
down remote daemon.
16/07/18 23:30:12 INFO RemoteActorRefProvider$RemotingTerminator:Remotedaemonshutdown;proceedingwithflushingremotetransports.16/07/1823:30:12INFORemoteActorRefProvider$RemotingTerminator: Remoting
shut down.

Process finished with exit code 0