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   * 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,
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   * See the License for the specific language governing permissions and
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  package org.apache.spark.mllib.tree;
  
  import java.util.HashMap;
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.tree.configuration.Algo;
  import org.apache.spark.mllib.tree.configuration.Strategy;
  import org.apache.spark.mllib.tree.impurity.Gini;
  import org.apache.spark.mllib.tree.model.DecisionTreeModel;
  
  public class JavaDecisionTreeSuite extends SharedSparkSession {
  
    int validatePrediction(List<LabeledPoint> validationData, DecisionTreeModel model) {
      int numCorrect = 0;
      for (LabeledPoint point : validationData) {
        Double prediction = model.predict(point.features());
        if (prediction == point.label()) {
          numCorrect++;
        }
      }
      return numCorrect;
    }
  
    @Test
    public void runDTUsingConstructor() {
      List<LabeledPoint> arr = DecisionTreeSuite.generateCategoricalDataPointsAsJavaList();
      JavaRDD<LabeledPoint> rdd = jsc.parallelize(arr);
      HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
      categoricalFeaturesInfo.put(1, 2); // feature 1 has 2 categories
  
      int maxDepth = 4;
      int numClasses = 2;
      int maxBins = 100;
      Strategy strategy = new Strategy(Algo.Classification(), Gini.instance(), maxDepth, numClasses,
        maxBins, categoricalFeaturesInfo);
  
      DecisionTree learner = new DecisionTree(strategy);
      DecisionTreeModel model = learner.run(rdd.rdd());
  
      int numCorrect = validatePrediction(arr, model);
      Assert.assertTrue(numCorrect == rdd.count());
    }
  
    @Test
    public void runDTUsingStaticMethods() {
      List<LabeledPoint> arr = DecisionTreeSuite.generateCategoricalDataPointsAsJavaList();
      JavaRDD<LabeledPoint> rdd = jsc.parallelize(arr);
      HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
      categoricalFeaturesInfo.put(1, 2); // feature 1 has 2 categories
  
      int maxDepth = 4;
      int numClasses = 2;
      int maxBins = 100;
      Strategy strategy = new Strategy(Algo.Classification(), Gini.instance(), maxDepth, numClasses,
        maxBins, categoricalFeaturesInfo);
  
      DecisionTreeModel model = DecisionTree$.MODULE$.train(rdd.rdd(), strategy);
  
      // java compatibility test
      JavaRDD<Double> predictions = model.predict(rdd.map(new Function<LabeledPoint, Vector>() {
        @Override
        public Vector call(LabeledPoint v1) {
          return v1.features();
        }
      }));
  
      int numCorrect = validatePrediction(arr, model);
      Assert.assertTrue(numCorrect == rdd.count());
    }
  
  }
  