【云计算与大数据技术】Spark实战项目之判别西瓜好坏（附源码和数据集）

import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.{SparkConf, SparkContext}
object SparkGBT {
  def main (args: Array[String]) {
    if (args.length < 0) {
      println("Usage:FilePath")
      sys.exit(1)
    }
    //Initialization
    val conf = new SparkConf().setAppName("Spark MLlib Exercise: GradientBoostedTree")
    val sc = new SparkContext(conf)
    // Load and parse the data file.
    val data = MLUtils.loadLibSVMFile(sc, "/home/liujun/workplace/scala_GBT/GBT_data.txt")
    // Split the data into training and test sets (30% held out for testing)
    val splits = data.randomSplit(Array(0.7, 0.3))
    val (trainingData, testData) = (splits(0), splits(1))
    // Train a GradientBoostedTrees model.
    // The defaultParams for Classification use LogLoss by default.
    val boostingStrategy = BoostingStrategy.defaultParams("Classification")
    boostingStrategy.numIterations = 10 // Note: Use more iterations in practice.
    boostingStrategy.treeStrategy.numClasses = 2
    boostingStrategy.treeStrategy.maxDepth = 3
    // Empty categoricalFeaturesInfo indicates all features are continuous.
    boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]()
(r => r._1 != r._2).count.toDouble / testData.count()
    println("Test Error = " + testErr)
    println("Learned classification GBT model:\n" + model.toDebugString)
    labelAndPreds.collect().foreach(x =>
      println("Lable and Prediction: " + x._1.toString + " " + x._2.toString))
    trainingData.saveAsTextFile("/home/liujun/workplace/scala_GBT/trainingData")
    testData.saveAsTextFile("/home/liujun/workplace/scala_GBT/testData")
  }
}