随机森林（1）

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.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");
  JavaSparkContext jsc = 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/sample_libsvm_data.txt";
  JavaRDD<LabeledPoint> data = MLUtils . loadLibSVMFile(jsc.sc(), datapath)
  . toJavaRDD() ;
  // Split the data into training and test sets (30% held out for testing)
  JavaRDD<LabeledPoint>[] splits = data. 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 = 2;
  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 = 32;
  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(new PairFunction<LabeledPoint,Double, Double>() {
      @Override
      public Tuple2<Double, Double> call(LabeledPoint p) {
      return new Tuple2<Double, Double> (model. predict(p. features()), p.label());
      }
      });
      Double testErr =
      1.0 * predictionAndLabel. filter(new Function<Tuple2<Double,Double>, Boolean>() {
      public Boolean call(Tuple2<Double,Double> 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());
  }
}