Example usage for org.apache.mahout.clustering ClusteringUtils daviesBouldinIndex

Introduction

In this page you can find the example usage for org.apache.mahout.clustering ClusteringUtils daviesBouldinIndex.

Prototype

public static double daviesBouldinIndex(List<? extends Vector> centroids, DistanceMeasure distanceMeasure,
        List<OnlineSummarizer> clusterDistanceSummaries) 

Source Link

Document

Computes the Davies-Bouldin Index for a given clustering.

Usage

From source file:org.conan.mymahout.clustering.streaming.tools.ClusterQualitySummarizer.java

License:Apache License

public int run(String[] args) throws IOException {
    if (!parseArgs(args)) {
        return -1;
    }/*from   www  .  j  a  v a  2s. c  o m*/

    Configuration conf = new Configuration();
    try {
        //      Configuration.dumpConfiguration(conf, new OutputStreamWriter(System.out));

        fileOut = new PrintWriter(new FileOutputStream(outputFile));
        fileOut.printf("cluster,distance.mean,distance.sd,distance.q0,distance.q1,distance.q2,distance.q3,"
                + "distance.q4,count,is.train\n");

        // Reading in the centroids (both pairs, if they exist).
        List<Centroid> centroids;
        List<Centroid> centroidsCompare = null;
        if (mahoutKMeansFormat) {
            SequenceFileDirValueIterable<ClusterWritable> clusterIterable = new SequenceFileDirValueIterable<ClusterWritable>(
                    new Path(centroidFile), PathType.GLOB, conf);
            centroids = Lists.newArrayList(IOUtils.getCentroidsFromClusterWritableIterable(clusterIterable));
        } else {
            SequenceFileDirValueIterable<CentroidWritable> centroidIterable = new SequenceFileDirValueIterable<CentroidWritable>(
                    new Path(centroidFile), PathType.GLOB, conf);
            centroids = Lists.newArrayList(IOUtils.getCentroidsFromCentroidWritableIterable(centroidIterable));
        }

        if (centroidCompareFile != null) {
            if (mahoutKMeansFormatCompare) {
                SequenceFileDirValueIterable<ClusterWritable> clusterCompareIterable = new SequenceFileDirValueIterable<ClusterWritable>(
                        new Path(centroidCompareFile), PathType.GLOB, conf);
                centroidsCompare = Lists
                        .newArrayList(IOUtils.getCentroidsFromClusterWritableIterable(clusterCompareIterable));
            } else {
                SequenceFileDirValueIterable<CentroidWritable> centroidCompareIterable = new SequenceFileDirValueIterable<CentroidWritable>(
                        new Path(centroidCompareFile), PathType.GLOB, conf);
                centroidsCompare = Lists.newArrayList(
                        IOUtils.getCentroidsFromCentroidWritableIterable(centroidCompareIterable));
            }
        }

        // Reading in the "training" set.
        SequenceFileDirValueIterable<VectorWritable> trainIterable = new SequenceFileDirValueIterable<VectorWritable>(
                new Path(trainFile), PathType.GLOB, conf);
        Iterable<Vector> trainDatapoints = IOUtils.getVectorsFromVectorWritableIterable(trainIterable);
        Iterable<Vector> datapoints = trainDatapoints;

        printSummaries(ClusteringUtils.summarizeClusterDistances(trainDatapoints, centroids,
                new SquaredEuclideanDistanceMeasure()), "train");

        // Also adding in the "test" set.
        if (testFile != null) {
            SequenceFileDirValueIterable<VectorWritable> testIterable = new SequenceFileDirValueIterable<VectorWritable>(
                    new Path(testFile), PathType.GLOB, conf);
            Iterable<Vector> testDatapoints = IOUtils.getVectorsFromVectorWritableIterable(testIterable);

            printSummaries(ClusteringUtils.summarizeClusterDistances(testDatapoints, centroids,
                    new SquaredEuclideanDistanceMeasure()), "test");

            datapoints = Iterables.concat(trainDatapoints, testDatapoints);
        }

        // At this point, all train/test CSVs have been written. We now compute quality metrics.
        List<OnlineSummarizer> summaries = ClusteringUtils.summarizeClusterDistances(datapoints, centroids,
                distanceMeasure);
        List<OnlineSummarizer> compareSummaries = null;
        if (centroidsCompare != null) {
            compareSummaries = ClusteringUtils.summarizeClusterDistances(datapoints, centroidsCompare,
                    distanceMeasure);
        }
        System.out.printf("[Dunn Index] First: %f",
                ClusteringUtils.dunnIndex(centroids, distanceMeasure, summaries));
        if (compareSummaries != null) {
            System.out.printf(" Second: %f\n",
                    ClusteringUtils.dunnIndex(centroidsCompare, distanceMeasure, compareSummaries));
        } else {
            System.out.printf("\n");
        }
        System.out.printf("[Davies-Bouldin Index] First: %f",
                ClusteringUtils.daviesBouldinIndex(centroids, distanceMeasure, summaries));
        if (compareSummaries != null) {
            System.out.printf(" Second: %f\n",
                    ClusteringUtils.daviesBouldinIndex(centroidsCompare, distanceMeasure, compareSummaries));
        } else {
            System.out.printf("\n");
        }
    } catch (IOException e) {
        System.out.println(e.getMessage());
    } finally {
        Closeables.close(fileOut, false);
    }
    return 0;
}

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