content: /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
   * the License.  You may obtain a copy of the License at
   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package org.apache.spark.examples.mllib;
  
  // $example on$
  import java.util.HashMap;
  import java.util.Map;
  
  import scala.Tuple2;
  
  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.Function2;
  import org.apache.spark.api.java.function.PairFunction;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.tree.DecisionTree;
  import org.apache.spark.mllib.tree.model.DecisionTreeModel;
  import org.apache.spark.mllib.util.MLUtils;
  // $example off$
  
  class JavaDecisionTreeRegressionExample {
  
    public static void main(String[] args) {
  
      // $example on$
      SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTreeRegressionExample");
      JavaSparkContext jsc = new JavaSparkContext(sparkConf);
  
      // Load and parse the data file.
      String datapath = "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];
  
      // Set parameters.
      // Empty categoricalFeaturesInfo indicates all features are continuous.
      Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
      String impurity = "variance";
      Integer maxDepth = 5;
      Integer maxBins = 32;
  
      // Train a DecisionTree model.
      final DecisionTreeModel model = DecisionTree.trainRegressor(trainingData,
        categoricalFeaturesInfo, impurity, maxDepth, maxBins);
  
      // 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<>(model.predict(p.features()), p.label());
        }
      });
      Double testMSE =
        predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
          @Override
          public Double call(Tuple2<Double, Double> pl) {
            Double diff = pl._1() - pl._2();
            return diff * diff;
          }
        }).reduce(new Function2<Double, Double, Double>() {
          @Override
          public Double call(Double a, Double b) {
            return a + b;
          }
        }) / data.count();
      System.out.println("Test Mean Squared Error: " + testMSE);
      System.out.println("Learned regression tree model:\n" + model.toDebugString());
  
      // Save and load model
      model.save(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
      DecisionTreeModel sameModel = DecisionTreeModel
        .load(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
      // $example off$
    }
  }
  