Classification and regression using decision trees in apache spark - Java Big Data

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Description

Classification and regression using decision trees in apache spark

Demo Code

/*//w  w  w  .jav a  2s  . co  m
 * 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
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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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package eduonix.spark.mllib;

import java.util.HashMap;

import scala.Tuple2;

import org.apache.spark.api.java.function.Function2;
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.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;

/**
 * Classification and regression using decision trees.
 */
public final class JavaDecisionTree {

    public static void main(String[] args) {
        String datapath = "data/mllib/sample_libsvm_data.txt";
        if (args.length == 1) {
            datapath = args[0];
        } else if (args.length > 1) {
            System.err
                    .println("Usage: JavaDecisionTree <libsvm format data file>");
            System.exit(1);
        }
        SparkConf sparkConf = new SparkConf()
                .setAppName("JavaDecisionTree");
        JavaSparkContext sc = new JavaSparkContext(sparkConf);

        JavaRDD<LabeledPoint> data = MLUtils
                .loadLibSVMFile(sc.sc(), datapath).toJavaRDD().cache();

        // Compute the number of classes from the data.
        Integer numClasses = data.map(new Function<LabeledPoint, Double>() {
            @Override
            public Double call(LabeledPoint p) {
                return p.label();
            }
        }).countByValue().size();

        // Set parameters.
        //  Empty categoricalFeaturesInfo indicates all features are continuous.
        HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
        String impurity = "gini";
        Integer maxDepth = 5;
        Integer maxBins = 32;

        // Train a DecisionTree model for classification.
        final DecisionTreeModel model = DecisionTree.trainClassifier(data,
                numClasses, categoricalFeaturesInfo, impurity, maxDepth,
                maxBins);

        // Evaluate model on training instances and compute training error
        JavaPairRDD<Double, Double> predictionAndLabel = data
                .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 trainErr = 1.0
                * predictionAndLabel.filter(
                        new Function<Tuple2<Double, Double>, Boolean>() {
                            @Override
                            public Boolean call(Tuple2<Double, Double> pl) {
                                return !pl._1().equals(pl._2());
                            }
                        }).count() / data.count();
        System.out.println("Training error: " + trainErr);
        System.out.println("Learned classification tree model:\n" + model);

        // Train a DecisionTree model for regression.
        impurity = "variance";
        final DecisionTreeModel regressionModel = DecisionTree
                .trainRegressor(data, categoricalFeaturesInfo, impurity,
                        maxDepth, maxBins);

        // Evaluate model on training instances and compute training error
        JavaPairRDD<Double, Double> regressorPredictionAndLabel = data
                .mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
                    @Override
                    public Tuple2<Double, Double> call(LabeledPoint p) {
                        return new Tuple2<Double, Double>(regressionModel
                                .predict(p.features()), p.label());
                    }
                });
        Double trainMSE = regressorPredictionAndLabel.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("Training Mean Squared Error: " + trainMSE);
        System.out.println("Learned regression tree model:\n"
                + regressionModel);

        sc.stop();
    }
}

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