Spark中组件Mllib的学习40之梯度提升树（GBT）用于回归_.md

更多代码请见：https://github.com/xubo245/SparkLearning
Spark中组件Mllib的学习之分类篇
1解释
GBRT（Gradient Boost Regression Tree）渐进梯度回归树
同样的setCategoricalFeaturesInfo有问题。注释掉了。

2.代码：

```
/**
  * @author xubo
  *         ref:Spark MlLib机器学习实战
  *         more code:https://github.com/xubo245/SparkLearning
  *         more blog:http://blog.csdn.net/xubo245
  */
package org.apache.spark.mllib.learning.classification

import java.text.SimpleDateFormat
import java.util.Date

import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.BoostingStrategy
import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel}
import org.apache.spark.mllib.util.MLUtils
import java.util.Map
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

/**
  * Created by xubo on 2016/5/23.
  */
object GBTs2Regression {
  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster("local[4]").setAppName(this.getClass().getSimpleName().filter(!_.equals('$')))
    val sc = new SparkContext(conf)

    // Load and parse the data file.
    val data = MLUtils.loadLibSVMFile(sc, "file/data/mllib/input/classification/sample_libsvm_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("Regression")
    boostingStrategy.setNumIterations(3)
    boostingStrategy.treeStrategy.setMaxDepth(5)
    //    boostingStrategy.treeStrategy.setCategoricalFeaturesInfo(Map[Int, Int]())

    // Train a GradientBoostedTrees model.
    //  The defaultParams for Regression use SquaredError by default.
    //    val boostingStrategy = BoostingStrategy.defaultParams("Regression")
    //    boostingStrategy.numIterations = 3 // Note: Use more iterations in practice.
    //    boostingStrategy.treeStrategy.maxDepth = 5
    //    //  Empty categoricalFeaturesInfo indicates all features are continuous.
    //    boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]()

    val model = GradientBoostedTrees.train(trainingData, boostingStrategy)

    // Evaluate model on test instances and compute test error
    val labelsAndPredictions = testData.map { point =>
      val prediction = model.predict(point.features)
      (point.label, prediction)
    }
    val testMSE = labelsAndPredictions.map { case (v, p) => math.pow((v - p), 2) }.mean()
    println("Test Mean Squared Error = " + testMSE)
    println("Learned regression GBT model:\n" + model.toDebugString)



    println("data.count:" + data.count())
    println("trainingData.count:" + trainingData.count())
    println("testData.count:" + testData.count())
    println("model.algo:" + model.algo)
    println("model.trees:" + model.trees)
    println("model.treeWeights:" + model.treeWeights)



    //     Save and load model
    //    val iString = new SimpleDateFormat("yyyyMMddHHmmssSSS").format(new Date())
    //    val path = "file/data/mllib/output/classification/GradientBoostedTreesModel" + iString + "/result"
    //    model.save(sc, path)
    //    val sameModel = DecisionTreeModel.load(sc, path)
    //    println(sameModel.algo)
    sc.stop
  }
}

```

3.结果：

```
Test Mean Squared Error = 0.06896551724137932
Learned regression GBT model:
TreeEnsembleModel regressor with 3 trees

  Tree 0:
    If (feature 406 <= 72.0)
     If (feature 99 <= 0.0)
      Predict: 0.0
     Else (feature 99 > 0.0)
      Predict: 1.0
    Else (feature 406 > 72.0)
     Predict: 1.0
  Tree 1:
    Predict: 0.0
  Tree 2:
    Predict: 0.0

data.count:100
trainingData.count:71
testData.count:29
model.algo:Regression
model.trees:[Lorg.apache.spark.mllib.tree.model.DecisionTreeModel;@5e9a7c29
model.treeWeights:[D@78bf694d
```

参考 【1】http://spark.apache.org/docs/1.5.2/mllib-guide.html 【2】http://spark.apache.org/docs/1.5.2/programming-guide.html 【3】https://github.com/xubo245/SparkLearning