Examples with EvaluationResult de.lmu.ifi.dbs.elki.result.EvaluationResult used on opensource projects

Example 1 with EvaluationResult

use of de.lmu.ifi.dbs.elki.result.EvaluationResult in project elki by elki-project.

the class EvaluateVarianceRatioCriteria method evaluateClustering.

/**
 * Evaluate a single clustering.
 *
 * @param db Database
 * @param rel Data relation
 * @param c Clustering
 * @return Variance Ratio Criteria
 */
public double evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) {
    // FIXME: allow using a precomputed distance matrix!
    final SquaredEuclideanDistanceFunction df = SquaredEuclideanDistanceFunction.STATIC;
    List<? extends Cluster<?>> clusters = c.getAllClusters();
    double vrc = 0.;
    int ignorednoise = 0;
    if (clusters.size() > 1) {
        NumberVector[] centroids = new NumberVector[clusters.size()];
        ignorednoise = EvaluateSimplifiedSilhouette.centroids(rel, clusters, centroids, noiseOption);
        // Build global centroid and cluster count:
        final int dim = RelationUtil.dimensionality(rel);
        Centroid overallCentroid = new Centroid(dim);
        int clustercount = globalCentroid(overallCentroid, rel, clusters, centroids, noiseOption);
        // a: Distance to own centroid
        // b: Distance to overall centroid
        double a = 0, b = 0;
        Iterator<? extends Cluster<?>> ci = clusters.iterator();
        for (int i = 0; ci.hasNext(); i++) {
            Cluster<?> cluster = ci.next();
            if (cluster.size() <= 1 || cluster.isNoise()) {
                switch(noiseOption) {
                    case IGNORE_NOISE:
                        // Ignored
                        continue;
                    case TREAT_NOISE_AS_SINGLETONS:
                        // Singletons: a = 0 by definition.
                        for (DBIDIter it = cluster.getIDs().iter(); it.valid(); it.advance()) {
                            b += df.distance(overallCentroid, rel.get(it));
                        }
                        // with NEXT cluster.
                        continue;
                    case MERGE_NOISE:
                        // Treat like a cluster below:
                        break;
                }
            }
            for (DBIDIter it = cluster.getIDs().iter(); it.valid(); it.advance()) {
                NumberVector vec = rel.get(it);
                a += df.distance(centroids[i], vec);
                b += df.distance(overallCentroid, vec);
            }
        }
        vrc = ((b - a) / a) * ((rel.size() - clustercount) / (clustercount - 1.));
        // Only if {@link NoiseHandling#IGNORE_NOISE}:
        if (penalize && ignorednoise > 0) {
            vrc *= (rel.size() - ignorednoise) / (double) rel.size();
        }
    }
    if (LOG.isStatistics()) {
        LOG.statistics(new StringStatistic(key + ".vrc.noise-handling", noiseOption.toString()));
        if (ignorednoise > 0) {
            LOG.statistics(new LongStatistic(key + ".vrc.ignored", ignorednoise));
        }
        LOG.statistics(new DoubleStatistic(key + ".vrc", vrc));
    }
    EvaluationResult ev = EvaluationResult.findOrCreate(db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
    MeasurementGroup g = ev.findOrCreateGroup("Distance-based Evaluation");
    g.addMeasure("Variance Ratio Criteria", vrc, 0., 1., 0., false);
    return vrc;
}

Example 2 with EvaluationResult

use of de.lmu.ifi.dbs.elki.result.EvaluationResult in project elki by elki-project.

the class EvaluationVisualization method processNewResult.

@Override
public void processNewResult(VisualizerContext context, Object start) {
    VisualizationTree.findNewResults(context, start).filter(EvaluationResult.class).forEach(sr -> {
        // Avoid duplicates:
        for (It<VisualizationTask> it2 = VisualizationTree.findVis(context, sr).filter(VisualizationTask.class); it2.valid(); it2.advance()) {
            if (it2.get().getFactory() instanceof EvaluationVisualization) {
                return;
            }
        }
        // Hack: for clusterings, only show the currently visible clustering.
        if (sr.visualizeSingleton()) {
            Class<? extends EvaluationResult> c = sr.getClass();
            // Ensure singleton.
            for (It<VisualizationTask> it3 = context.getVisHierarchy().iterChildren(context.getBaseResult()).filter(VisualizationTask.class); it3.valid(); it3.advance()) {
                final VisualizationTask otask = it3.get();
                if (otask.getFactory() instanceof EvaluationVisualization && otask.getResult() == c) {
                    return;
                }
            }
            context.addVis(context.getBaseResult(), // 
            new VisualizationTask(this, NAME, c, null).requestSize(.5, // 
            sr.numLines() * .05).level(// 
            VisualizationTask.LEVEL_STATIC).with(UpdateFlag.ON_STYLEPOLICY));
            return;
        }
        context.addVis(sr, // 
        new VisualizationTask(this, NAME, sr, null).requestSize(.5, sr.numLines() * .05).level(VisualizationTask.LEVEL_STATIC));
    });
}

Example 3 with EvaluationResult

use of de.lmu.ifi.dbs.elki.result.EvaluationResult in project elki by elki-project.

the class EvaluationVisualization method makeVisualization.

@Override
public Visualization makeVisualization(VisualizerContext context, VisualizationTask task, VisualizationPlot plot, double width, double height, Projection proj) {
    // TODO: make a utility class to wrap SVGPlot + parent layer + ypos.
    // TODO: use CSSClass and StyleLibrary
    // Skip space before first header
    double ypos = -.5;
    Element parent = plot.svgElement(SVGConstants.SVG_G_TAG);
    Object o = task.getResult();
    EvaluationResult sr = null;
    if (o instanceof EvaluationResult) {
        sr = (EvaluationResult) o;
    } else if (o instanceof Class && EvaluationResult.class.isAssignableFrom((Class<?>) o)) {
        // Use cluster evaluation of current style instead.
        StylingPolicy spol = context.getStylingPolicy();
        if (spol instanceof ClusterStylingPolicy) {
            ClusterStylingPolicy cpol = (ClusterStylingPolicy) spol;
            // will be a subtype, actually!
            @SuppressWarnings("unchecked") Class<EvaluationResult> c = (Class<EvaluationResult>) o;
            for (It<EvaluationResult> it = VisualizationTree.findNewResults(context, cpol.getClustering()).filter(c); it.valid(); it.advance()) {
                // may end up displaying the wrong evaluation.
                if (context.getHierarchy().iterAncestors(it.get()).find(cpol.getClustering())) {
                    sr = it.get();
                    break;
                }
            }
        }
    }
    if (sr == null) {
        // Failed.
        return new StaticVisualizationInstance(context, task, plot, width, height, parent);
    }
    for (String header : sr.getHeaderLines()) {
        ypos = addHeader(plot, parent, ypos, header);
    }
    for (EvaluationResult.MeasurementGroup g : sr) {
        ypos = addHeader(plot, parent, ypos, g.getName());
        for (EvaluationResult.Measurement m : g) {
            ypos = addBarChart(plot, parent, ypos, m.getName(), m.getVal(), m.getMin(), m.getMax(), m.getExp(), m.lowerIsBetter());
        }
    }
    // scale vis
    double cols = 10;
    final StyleLibrary style = context.getStyleLibrary();
    final double margin = style.getSize(StyleLibrary.MARGIN);
    final String transform = SVGUtil.makeMarginTransform(width, height, cols, ypos, margin / StyleLibrary.SCALE);
    SVGUtil.setAtt(parent, SVGConstants.SVG_TRANSFORM_ATTRIBUTE, transform);
    return new StaticVisualizationInstance(context, task, plot, width, height, parent);
}

Example 4 with EvaluationResult

use of de.lmu.ifi.dbs.elki.result.EvaluationResult in project elki by elki-project.

the class EvaluateConcordantPairs method evaluateClustering.

/**
 * Evaluate a single clustering.
 *
 * @param db Database
 * @param rel Data relation
 * @param c Clustering
 * @return Gamma index
 */
public double evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) {
    List<? extends Cluster<?>> clusters = c.getAllClusters();
    int ignorednoise = 0, withinPairs = 0;
    for (Cluster<?> cluster : clusters) {
        if ((cluster.size() <= 1 || cluster.isNoise())) {
            switch(noiseHandling) {
                case IGNORE_NOISE:
                    ignorednoise += cluster.size();
                    continue;
                case TREAT_NOISE_AS_SINGLETONS:
                    // No concordant distances.
                    continue;
                case MERGE_NOISE:
                    // Treat like a cluster below.
                    break;
            }
        }
        withinPairs += (cluster.size() * (cluster.size() - 1)) >>> 1;
        if (withinPairs < 0) {
            throw new AbortException("Integer overflow - clusters too large to compute pairwise distances.");
        }
    }
    // Materialize within-cluster distances (sorted):
    double[] withinDistances = computeWithinDistances(rel, clusters, withinPairs);
    int[] withinTies = new int[withinDistances.length];
    // Count ties within
    countTies(withinDistances, withinTies);
    long concordantPairs = 0, discordantPairs = 0, betweenPairs = 0;
    // Step two, compute discordant distances:
    for (int i = 0; i < clusters.size(); i++) {
        Cluster<?> ocluster1 = clusters.get(i);
        if (// 
        (ocluster1.size() <= 1 || ocluster1.isNoise()) && noiseHandling.equals(NoiseHandling.IGNORE_NOISE)) {
            continue;
        }
        for (int j = i + 1; j < clusters.size(); j++) {
            Cluster<?> ocluster2 = clusters.get(j);
            if (// 
            (ocluster2.size() <= 1 || ocluster2.isNoise()) && noiseHandling.equals(NoiseHandling.IGNORE_NOISE)) {
                continue;
            }
            betweenPairs += ocluster1.size() * ocluster2.size();
            for (DBIDIter oit1 = ocluster1.getIDs().iter(); oit1.valid(); oit1.advance()) {
                NumberVector obj = rel.get(oit1);
                for (DBIDIter oit2 = ocluster2.getIDs().iter(); oit2.valid(); oit2.advance()) {
                    double dist = distanceFunction.distance(obj, rel.get(oit2));
                    int p = Arrays.binarySearch(withinDistances, dist);
                    if (p >= 0) {
                        // Tied distances:
                        while (p > 0 && withinDistances[p - 1] >= dist) {
                            --p;
                        }
                        concordantPairs += p;
                        discordantPairs += withinDistances.length - p - withinTies[p];
                        continue;
                    }
                    p = -p - 1;
                    concordantPairs += p;
                    discordantPairs += withinDistances.length - p;
                }
            }
        }
    }
    // Total number of pairs possible:
    final long t = ((rel.size() - ignorednoise) * (long) (rel.size() - ignorednoise - 1)) >>> 1;
    final long tt = (t * (t - 1)) >>> 1;
    double gamma = (concordantPairs - discordantPairs) / (double) (concordantPairs + discordantPairs);
    double tau = computeTau(concordantPairs, discordantPairs, tt, withinDistances.length, betweenPairs);
    // Avoid NaN when everything is in a single cluster:
    gamma = gamma > 0. ? gamma : 0.;
    tau = tau > 0. ? tau : 0.;
    if (LOG.isStatistics()) {
        LOG.statistics(new StringStatistic(key + ".pbm.noise-handling", noiseHandling.toString()));
        if (ignorednoise > 0) {
            LOG.statistics(new LongStatistic(key + ".pbm.ignored", ignorednoise));
        }
        LOG.statistics(new DoubleStatistic(key + ".gamma", gamma));
        LOG.statistics(new DoubleStatistic(key + ".tau", tau));
    }
    EvaluationResult ev = EvaluationResult.findOrCreate(db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
    MeasurementGroup g = ev.findOrCreateGroup("Concordance-based Evaluation");
    g.addMeasure("Gamma", gamma, -1., 1., 0., false);
    g.addMeasure("Tau", tau, -1., +1., 0., false);
    db.getHierarchy().resultChanged(ev);
    return gamma;
}

Example 5 with EvaluationResult

use of de.lmu.ifi.dbs.elki.result.EvaluationResult in project elki by elki-project.

the class EvaluateDaviesBouldin method evaluateClustering.

/**
 * Evaluate a single clustering.
 *
 * @param db Database
 * @param rel Data relation
 * @param c Clustering
 * @return DB-index
 */
public double evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) {
    List<? extends Cluster<?>> clusters = c.getAllClusters();
    NumberVector[] centroids = new NumberVector[clusters.size()];
    int noisecount = EvaluateSimplifiedSilhouette.centroids(rel, clusters, centroids, noiseOption);
    double[] withinGroupDistance = withinGroupDistances(rel, clusters, centroids);
    Mean daviesBouldin = new Mean();
    for (int i = 0; i < clusters.size(); i++) {
        final NumberVector centroid = centroids[i];
        final double withinGroupDistancei = withinGroupDistance[i];
        // maximum within-to-between cluster spread
        double max = 0;
        for (int j = 0; j < clusters.size(); j++) {
            NumberVector ocentroid = centroids[j];
            if (ocentroid == centroid) {
                continue;
            }
            // Both are real clusters:
            if (centroid != null && ocentroid != null) {
                // bD = between group distance
                double bD = distanceFunction.distance(centroid, ocentroid);
                // d = within-to-between cluster spread
                double d = (withinGroupDistancei + withinGroupDistance[j]) / bD;
                max = d > max ? d : max;
            } else if (noiseOption != NoiseHandling.IGNORE_NOISE) {
                if (centroid != null) {
                    double d = Double.POSITIVE_INFINITY;
                    // Find the closest element
                    for (DBIDIter it = clusters.get(j).getIDs().iter(); it.valid(); it.advance()) {
                        double d2 = distanceFunction.distance(centroid, rel.get(it));
                        d = d2 < d ? d2 : d;
                    }
                    d = withinGroupDistancei / d;
                    max = d > max ? d : max;
                } else if (ocentroid != null) {
                    double d = Double.POSITIVE_INFINITY;
                    // Find the closest element
                    for (DBIDIter it = clusters.get(i).getIDs().iter(); it.valid(); it.advance()) {
                        double d2 = distanceFunction.distance(rel.get(it), ocentroid);
                        d = d2 < d ? d2 : d;
                    }
                    d = withinGroupDistance[j] / d;
                    max = d > max ? d : max;
                }
            // else: (0+0) / d = 0.
            }
        }
        daviesBouldin.put(max);
    }
    // For a single cluster, we return 2 (result for equidistant points)
    final double daviesBouldinMean = daviesBouldin.getCount() > 1 ? daviesBouldin.getMean() : 2.;
    if (LOG.isStatistics()) {
        LOG.statistics(new StringStatistic(key + ".db-index.noise-handling", noiseOption.toString()));
        if (noisecount > 0) {
            LOG.statistics(new LongStatistic(key + ".db-index.ignored", noisecount));
        }
        LOG.statistics(new DoubleStatistic(key + ".db-index", daviesBouldinMean));
    }
    EvaluationResult ev = EvaluationResult.findOrCreate(db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
    MeasurementGroup g = ev.findOrCreateGroup("Distance-based Evaluation");
    g.addMeasure("Davies Bouldin Index", daviesBouldinMean, 0., Double.POSITIVE_INFINITY, 0., true);
    db.getHierarchy().resultChanged(ev);
    return daviesBouldinMean;
}