Examples with MultipleObjectsBundleDatabaseConnection de.lmu.ifi.dbs.elki.datasource.MultipleObjectsBundleDatabaseConnection used on opensource projects

Example 1 with MultipleObjectsBundleDatabaseConnection

use of de.lmu.ifi.dbs.elki.datasource.MultipleObjectsBundleDatabaseConnection in project elki by elki-project.

the class ClassifierHoldoutEvaluationTask method run.

@Override
public void run() {
    Duration ptime = LOG.newDuration("evaluation.time.load").begin();
    MultipleObjectsBundle allData = databaseConnection.loadData();
    holdout.initialize(allData);
    LOG.statistics(ptime.end());
    Duration time = LOG.newDuration("evaluation.time.total").begin();
    ArrayList<ClassLabel> labels = holdout.getLabels();
    int[][] confusion = new int[labels.size()][labels.size()];
    for (int p = 0; p < holdout.numberOfPartitions(); p++) {
        TrainingAndTestSet partition = holdout.nextPartitioning();
        // Load the data set into a database structure (for indexing)
        Duration dur = LOG.newDuration(this.getClass().getName() + ".fold-" + (p + 1) + ".init.time").begin();
        Database db = new StaticArrayDatabase(new MultipleObjectsBundleDatabaseConnection(partition.getTraining()), indexFactories);
        db.initialize();
        LOG.statistics(dur.end());
        // Train the classifier
        dur = LOG.newDuration(this.getClass().getName() + ".fold-" + (p + 1) + ".train.time").begin();
        Relation<ClassLabel> lrel = db.getRelation(TypeUtil.CLASSLABEL);
        algorithm.buildClassifier(db, lrel);
        LOG.statistics(dur.end());
        // Evaluate the test set
        dur = LOG.newDuration(this.getClass().getName() + ".fold-" + (p + 1) + ".evaluation.time").begin();
        // FIXME: this part is still a big hack, unfortunately!
        MultipleObjectsBundle test = partition.getTest();
        int lcol = AbstractHoldout.findClassLabelColumn(test);
        int tcol = (lcol == 0) ? 1 : 0;
        for (int i = 0, l = test.dataLength(); i < l; ++i) {
            @SuppressWarnings("unchecked") O obj = (O) test.data(i, tcol);
            ClassLabel truelbl = (ClassLabel) test.data(i, lcol);
            ClassLabel predlbl = algorithm.classify(obj);
            int pred = Collections.binarySearch(labels, predlbl);
            int real = Collections.binarySearch(labels, truelbl);
            confusion[pred][real]++;
        }
        LOG.statistics(dur.end());
    }
    LOG.statistics(time.end());
    ConfusionMatrix m = new ConfusionMatrix(labels, confusion);
    LOG.statistics(m.toString());
}