binary classification in apache spark - Java Big Data

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binary classification in apache spark

Demo Code

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package eduonix.spark.mllib;

import scala.Tuple2;

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.Function2;
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;

public final class JavaRandomForestExample {

    /**
     * Note: This example illustrates binary classification.
     * For information on multiclass classification, please refer to the JavaDecisionTree.java
     * example.
     */
    private static void testClassification(
            JavaRDD<LabeledPoint> trainingData,
            JavaRDD<LabeledPoint> testData) {
        // 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 = 4;
        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>() {
                            @Override
                            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());
    }

    private static void testRegression(JavaRDD<LabeledPoint> trainingData,
            JavaRDD<LabeledPoint> testData) {
        // Train a RandomForest model.
        //  Empty categoricalFeaturesInfo indicates all features are continuous.
        HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
        Integer numTrees = 3; // Use more in practice.
        String featureSubsetStrategy = "auto"; // Let the algorithm choose.
        String impurity = "variance";
        Integer maxDepth = 4;
        Integer maxBins = 32;
        Integer seed = 12345;

        final RandomForestModel model = RandomForest.trainRegressor(
                trainingData, 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 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;
            }
        })
                / testData.count();
        System.out.println("Test Mean Squared Error: " + testMSE);
        System.out.println("Learned regression forest model:\n"
                + model.toDebugString());
    }

    public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf()
                .setAppName("JavaRandomForestExample");
        JavaSparkContext sc = new JavaSparkContext(sparkConf);

        // Load and parse the data file.
        String datapath = "data/mllib/sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.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];

        System.out
                .println("\nRunning example of classification using RandomForest\n");
        testClassification(trainingData, testData);

        System.out
                .println("\nRunning example of regression using RandomForest\n");
        testRegression(trainingData, testData);
        sc.stop();
    }
}

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