Examples with ROCEvaluation de.lmu.ifi.dbs.elki.evaluation.scores.ROCEvaluation used on opensource projects

Example 1 with ROCEvaluation

use of de.lmu.ifi.dbs.elki.evaluation.scores.ROCEvaluation in project elki by elki-project.

the class RankingQualityHistogram method run.

/**
 * Process a database
 *
 * @param database Database to process
 * @param relation Relation to process
 * @return Histogram of ranking qualities
 */
public HistogramResult run(Database database, Relation<O> relation) {
    final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, relation.size());
    if (LOG.isVerbose()) {
        LOG.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters();
    DoubleStaticHistogram hist = new DoubleStaticHistogram(numbins, 0.0, 1.0);
    if (LOG.isVerbose()) {
        LOG.verbose("Processing points...");
    }
    FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("Computing ROC AUC values", relation.size(), LOG) : null;
    ROCEvaluation roc = new ROCEvaluation();
    MeanVariance mv = new MeanVariance();
    // sort neighbors
    for (Cluster<?> clus : split) {
        for (DBIDIter iter = clus.getIDs().iter(); iter.valid(); iter.advance()) {
            KNNList knn = knnQuery.getKNNForDBID(iter, relation.size());
            double result = EvaluateClustering.evaluateRanking(roc, clus, knn);
            mv.put(result);
            hist.increment(result, 1. / relation.size());
            LOG.incrementProcessed(progress);
        }
    }
    LOG.ensureCompleted(progress);
    // Transform Histogram into a Double Vector array.
    Collection<double[]> res = new ArrayList<>(relation.size());
    for (DoubleStaticHistogram.Iter iter = hist.iter(); iter.valid(); iter.advance()) {
        res.add(new double[] { iter.getCenter(), iter.getValue() });
    }
    HistogramResult result = new HistogramResult("Ranking Quality Histogram", "ranking-histogram", res);
    result.addHeader("Mean: " + mv.getMean() + " Variance: " + mv.getSampleVariance());
    return result;
}

Example 2 with ROCEvaluation

use of de.lmu.ifi.dbs.elki.evaluation.scores.ROCEvaluation in project elki by elki-project.

the class EvaluateRankingQuality method run.

@Override
public HistogramResult run(Database database) {
    final Relation<V> relation = database.getRelation(getInputTypeRestriction()[0]);
    final DistanceQuery<V> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<V> knnQuery = database.getKNNQuery(distQuery, relation.size());
    if (LOG.isVerbose()) {
        LOG.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters();
    // Compute cluster averages and covariance matrix
    HashMap<Cluster<?>, double[]> averages = new HashMap<>(split.size());
    HashMap<Cluster<?>, double[][]> covmats = new HashMap<>(split.size());
    for (Cluster<?> clus : split) {
        CovarianceMatrix covmat = CovarianceMatrix.make(relation, clus.getIDs());
        averages.put(clus, covmat.getMeanVector());
        covmats.put(clus, covmat.destroyToPopulationMatrix());
    }
    MeanVarianceStaticHistogram hist = new MeanVarianceStaticHistogram(numbins, 0.0, 1.0);
    if (LOG.isVerbose()) {
        LOG.verbose("Processing points...");
    }
    FiniteProgress rocloop = LOG.isVerbose() ? new FiniteProgress("Computing ROC AUC values", relation.size(), LOG) : null;
    ROCEvaluation roc = new ROCEvaluation();
    // sort neighbors
    for (Cluster<?> clus : split) {
        ModifiableDoubleDBIDList cmem = DBIDUtil.newDistanceDBIDList(clus.size());
        double[] av = averages.get(clus);
        double[][] covm = covmats.get(clus);
        for (DBIDIter iter = clus.getIDs().iter(); iter.valid(); iter.advance()) {
            double d = mahalanobisDistance(covm, relation.get(iter).toArray(), av);
            cmem.add(d, iter);
        }
        cmem.sort();
        for (DBIDArrayIter it = cmem.iter(); it.valid(); it.advance()) {
            KNNList knn = knnQuery.getKNNForDBID(it, relation.size());
            double result = EvaluateClustering.evaluateRanking(roc, clus, knn);
            hist.put(((double) it.getOffset()) / clus.size(), result);
            LOG.incrementProcessed(rocloop);
        }
    }
    LOG.ensureCompleted(rocloop);
    // Collections.sort(results);
    // Transform Histogram into a Double Vector array.
    Collection<double[]> res = new ArrayList<>(relation.size());
    for (ObjHistogram.Iter<MeanVariance> iter = hist.iter(); iter.valid(); iter.advance()) {
        res.add(new double[] { iter.getCenter(), iter.getValue().getCount(), iter.getValue().getMean(), iter.getValue().getSampleVariance() });
    }
    return new HistogramResult("Ranking Quality Histogram", "ranking-histogram", res);
}