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

Example 6 with QuotientOutlierScoreMeta

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

the class OPTICSOF method run.

/**
 * Perform OPTICS-based outlier detection.
 *
 * @param database Database
 * @param relation Relation
 * @return Outlier detection result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    DistanceQuery<O> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
    KNNQuery<O> knnQuery = database.getKNNQuery(distQuery, minpts);
    RangeQuery<O> rangeQuery = database.getRangeQuery(distQuery);
    DBIDs ids = relation.getDBIDs();
    // FIXME: implicit preprocessor.
    WritableDataStore<KNNList> nMinPts = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, KNNList.class);
    WritableDoubleDataStore coreDistance = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    WritableIntegerDataStore minPtsNeighborhoodSize = DataStoreUtil.makeIntegerStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, -1);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        KNNList minptsNeighbours = knnQuery.getKNNForDBID(iditer, minpts);
        double d = minptsNeighbours.getKNNDistance();
        nMinPts.put(iditer, minptsNeighbours);
        coreDistance.putDouble(iditer, d);
        minPtsNeighborhoodSize.put(iditer, rangeQuery.getRangeForDBID(iditer, d).size());
    }
    // Pass 2
    WritableDataStore<List<Double>> reachDistance = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, List.class);
    WritableDoubleDataStore lrds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        List<Double> core = new ArrayList<>();
        double lrd = 0;
        // TODO: optimize for double distances
        for (DoubleDBIDListIter neighbor = nMinPts.get(iditer).iter(); neighbor.valid(); neighbor.advance()) {
            double coreDist = coreDistance.doubleValue(neighbor);
            double dist = distQuery.distance(iditer, neighbor);
            double rd = MathUtil.max(coreDist, dist);
            lrd = rd + lrd;
            core.add(rd);
        }
        lrd = minPtsNeighborhoodSize.intValue(iditer) / lrd;
        reachDistance.put(iditer, core);
        lrds.putDouble(iditer, lrd);
    }
    // Pass 3
    DoubleMinMax ofminmax = new DoubleMinMax();
    WritableDoubleDataStore ofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        double of = 0;
        for (DBIDIter neighbor = nMinPts.get(iditer).iter(); neighbor.valid(); neighbor.advance()) {
            double lrd = lrds.doubleValue(iditer);
            double lrdN = lrds.doubleValue(neighbor);
            of = of + lrdN / lrd;
        }
        of = of / minPtsNeighborhoodSize.intValue(iditer);
        ofs.putDouble(iditer, of);
        // update minimum and maximum
        ofminmax.put(of);
    }
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("OPTICS Outlier Scores", "optics-outlier", ofs, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(ofminmax.getMin(), ofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 7 with QuotientOutlierScoreMeta

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

the class CBLOF method run.

/**
 * Runs the CBLOF algorithm on the given database.
 *
 * @param database Database to query
 * @param relation Data to process
 * @return CBLOF outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("CBLOF", 3) : null;
    DBIDs ids = relation.getDBIDs();
    LOG.beginStep(stepprog, 1, "Computing clustering.");
    Clustering<MeanModel> clustering = clusteringAlgorithm.run(database);
    LOG.beginStep(stepprog, 2, "Computing boundary between large and small clusters.");
    List<? extends Cluster<MeanModel>> clusters = clustering.getAllClusters();
    Collections.sort(clusters, new Comparator<Cluster<MeanModel>>() {

        @Override
        public int compare(Cluster<MeanModel> o1, Cluster<MeanModel> o2) {
            // Sort in descending order by size
            return Integer.compare(o2.size(), o1.size());
        }
    });
    int clusterBoundary = getClusterBoundary(relation, clusters);
    List<? extends Cluster<MeanModel>> largeClusters = clusters.subList(0, clusterBoundary + 1);
    List<? extends Cluster<MeanModel>> smallClusters = clusters.subList(clusterBoundary + 1, clusters.size());
    LOG.beginStep(stepprog, 3, "Computing Cluster-Based Local Outlier Factors (CBLOF).");
    WritableDoubleDataStore cblofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB);
    DoubleMinMax cblofMinMax = new DoubleMinMax();
    computeCBLOFs(relation, distance, cblofs, cblofMinMax, largeClusters, smallClusters);
    LOG.setCompleted(stepprog);
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Cluster-Based Local Outlier Factor", "cblof-outlier", cblofs, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(cblofMinMax.getMin(), cblofMinMax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 8 with QuotientOutlierScoreMeta

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

the class IDOS method run.

/**
 * Run the algorithm
 *
 * @param database Database
 * @param relation Data relation
 * @return Outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("IDOS", 3) : null;
    if (stepprog != null) {
        stepprog.beginStep(1, "Precomputing neighborhoods", LOG);
    }
    KNNQuery<O> knnQ = DatabaseUtil.precomputedKNNQuery(database, relation, getDistanceFunction(), Math.max(k_c, k_r) + 1);
    DBIDs ids = relation.getDBIDs();
    if (stepprog != null) {
        stepprog.beginStep(2, "Computing intrinsic dimensionalities", LOG);
    }
    DoubleDataStore intDims = computeIDs(ids, knnQ);
    if (stepprog != null) {
        stepprog.beginStep(3, "Computing IDOS scores", LOG);
    }
    DoubleMinMax idosminmax = new DoubleMinMax();
    DoubleDataStore ldms = computeIDOS(ids, knnQ, intDims, idosminmax);
    if (stepprog != null) {
        stepprog.setCompleted(LOG);
    }
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Intrinsic Dimensionality Outlier Score", "idos", ldms, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(idosminmax.getMin(), idosminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 9 with QuotientOutlierScoreMeta

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

the class LOF method run.

/**
 * Runs the LOF algorithm on the given database.
 *
 * @param database Database to query
 * @param relation Data to process
 * @return LOF outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("LOF", 3) : null;
    DBIDs ids = relation.getDBIDs();
    LOG.beginStep(stepprog, 1, "Materializing nearest-neighbor sets.");
    KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, relation, getDistanceFunction(), k);
    // Compute LRDs
    LOG.beginStep(stepprog, 2, "Computing Local Reachability Densities (LRD).");
    WritableDoubleDataStore lrds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    computeLRDs(knnq, ids, lrds);
    // compute LOF_SCORE of each db object
    LOG.beginStep(stepprog, 3, "Computing Local Outlier Factors (LOF).");
    WritableDoubleDataStore lofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB);
    // track the maximum value for normalization.
    DoubleMinMax lofminmax = new DoubleMinMax();
    computeLOFScores(knnq, ids, lrds, lofs, lofminmax);
    LOG.setCompleted(stepprog);
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Local Outlier Factor", "lof-outlier", lofs, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(lofminmax.getMin(), lofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 10 with QuotientOutlierScoreMeta

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

the class COF method run.

/**
 * Runs the COF algorithm on the given database.
 *
 * @param database Database to query
 * @param relation Data to process
 * @return COF outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress("COF", 3) : null;
    DistanceQuery<O> dq = database.getDistanceQuery(relation, getDistanceFunction());
    LOG.beginStep(stepprog, 1, "Materializing COF neighborhoods.");
    KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, relation, dq, k);
    DBIDs ids = relation.getDBIDs();
    LOG.beginStep(stepprog, 2, "Computing Average Chaining Distances.");
    WritableDoubleDataStore acds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    computeAverageChainingDistances(knnq, dq, ids, acds);
    // compute COF_SCORE of each db object
    LOG.beginStep(stepprog, 3, "Computing Connectivity-based Outlier Factors.");
    WritableDoubleDataStore cofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB);
    // track the maximum value for normalization.
    DoubleMinMax cofminmax = new DoubleMinMax();
    computeCOFScores(knnq, ids, acds, cofs, cofminmax);
    LOG.setCompleted(stepprog);
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Connectivity-Based Outlier Factor", "cof-outlier", cofs, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(cofminmax.getMin(), cofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    return new OutlierResult(scoreMeta, scoreResult);
}