决策树代码共享

import java.util.HashMap;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.RandomForest;
import org.apache.spark.mllib.tree.model.RandomForestModel;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;
public class decisiontree1 {
  public static void main(String[] args) {
  SparkConf sparkConf = new SparkConf () . setAppName (" JavaRandomForestExample"); 
  sparkConf . setMaster("local[2]");
  JavaSparkContext sc = new JavaSparkContext (sparkConf); // Load and parse the data file.
  String datapath =
      "file:///home/gyq/下载/spark-2.3.2-bin-hadoop2.7/data/mllib/gg.txt";
  JavaRDD<String> data=sc.textFile(datapath);
  JavaRDD<LabeledPoint> parseData= data.map(f->{
    String[] parts=f.split(",");
    double[] v=new double[parts.length-1];
    for(int i=0;i<parts.length-1;i++) {
      v[i]=Double.valueOf(parts[i]);
    }
    double label=0.0;
    if (parts[parts.length-1].equals("Iris-versicolor")) {
      label=0.0;}
      else if(parts[parts.length-1].equals("Iris-setosa")){
      label=1.0;}
    else label=2.0;
    return new LabeledPoint(label,Vectors.dense(v));
  });
  parseData.foreach(f->System.out.println("label="+f.label()));
    JavaRDD<LabeledPoint>[] splits = parseData. randomSplit(new double[]{0.7, 0.3});
  JavaRDD<LabeledPoint> trainingData = splits[0];
  JavaRDD<LabeledPoint> testData = splits[1] ;
  // Train a RandomForest model.
  // Empty categoricalFeaturesInfo indicates all features are continuous .
  Integer numClasses = 3;
  HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
  Integer numTrees =3; // Use more in practice.
  String featureSubsetStrategy = "auto"; // Let the algorithm choose.
  String impurity = "gini";
  Integer maxDepth = 5;
  Integer maxBins = 10;
  Integer seed = 12345;
  final RandomForestModel model = RandomForest.trainClassifier(trainingData, numClasses,
      categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins,
      seed);
      // Evaluate model on test instances and compute test error
      JavaPairRDD<Double,Double> predictionAndLabel =
      testData. mapToPair(p->{
      return new Tuple2<Double, Double> (model. predict(p. features()), p.label());
      });
      Double testErr =1.0 * predictionAndLabel. filter(pl->{
      return !pl._1(). equals(pl._2());
      }).count()/testData.count();
      System. out. println("Test Error: "+ testErr);
      System. out. println("Learned classification forest model:\n" + model. toDebugString());
  }
}