Examples with AbortException de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException used on opensource projects

Example 56 with AbortException

use of de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException in project elki by elki-project.

the class KMLOutputHandler method processNewResult.

@Override
public void processNewResult(ResultHierarchy hier, Result newResult) {
    ArrayList<OutlierResult> ors = ResultUtil.filterResults(hier, newResult, OutlierResult.class);
    ArrayList<Clustering<?>> crs = ResultUtil.filterResults(hier, newResult, Clustering.class);
    if (ors.size() + crs.size() > 1) {
        throw new AbortException("More than one visualizable result found. The KML writer only supports a single result!");
    }
    Database database = ResultUtil.findDatabase(hier);
    for (OutlierResult outlierResult : ors) {
        try {
            XMLOutputFactory factory = XMLOutputFactory.newInstance();
            ZipOutputStream out = new ZipOutputStream(new FileOutputStream(filename));
            out.putNextEntry(new ZipEntry("doc.kml"));
            final XMLStreamWriter xmlw = factory.createXMLStreamWriter(out);
            writeOutlierResult(xmlw, outlierResult, database);
            xmlw.flush();
            xmlw.close();
            out.closeEntry();
            out.flush();
            out.close();
            if (autoopen) {
                Desktop.getDesktop().open(filename);
            }
        } catch (XMLStreamException e) {
            LOG.exception(e);
            throw new AbortException("XML error in KML output.", e);
        } catch (IOException e) {
            LOG.exception(e);
            throw new AbortException("IO error in KML output.", e);
        }
    }
    for (Clustering<?> clusteringResult : crs) {
        try {
            XMLOutputFactory factory = XMLOutputFactory.newInstance();
            ZipOutputStream out = new ZipOutputStream(new FileOutputStream(filename));
            out.putNextEntry(new ZipEntry("doc.kml"));
            final XMLStreamWriter xmlw = factory.createXMLStreamWriter(out);
            @SuppressWarnings("unchecked") Clustering<Model> cres = (Clustering<Model>) clusteringResult;
            writeClusteringResult(xmlw, cres, database);
            xmlw.flush();
            xmlw.close();
            out.closeEntry();
            out.flush();
            out.close();
            if (autoopen) {
                Desktop.getDesktop().open(filename);
            }
        } catch (XMLStreamException e) {
            LOG.exception(e);
            throw new AbortException("XML error in KML output.", e);
        } catch (IOException e) {
            LOG.exception(e);
            throw new AbortException("IO error in KML output.", e);
        }
    }
}

Example 57 with AbortException

use of de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException in project elki by elki-project.

the class GreedyEnsembleExperiment method run.

@Override
public void run() {
    // Note: the database contains the *result vectors*, not the original data.
    final Database database = inputstep.getDatabase();
    Relation<NumberVector> relation = database.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    final Relation<String> labels = DatabaseUtil.guessLabelRepresentation(database);
    final DBID firstid = DBIDUtil.deref(labels.iterDBIDs());
    final String firstlabel = labels.get(firstid);
    if (!firstlabel.matches("bylabel")) {
        throw new AbortException("No 'by label' reference outlier found, which is needed for weighting!");
    }
    relation = applyPrescaling(prescaling, relation, firstid);
    final int numcand = relation.size() - 1;
    // Dimensionality and reference vector
    final int dim = RelationUtil.dimensionality(relation);
    final NumberVector refvec = relation.get(firstid);
    // Build the positive index set for ROC AUC.
    VectorNonZero positive = new VectorNonZero(refvec);
    final int desired_outliers = (int) (rate * dim);
    int union_outliers = 0;
    final int[] outliers_seen = new int[dim];
    // Merge the top-k for each ensemble member, until we have enough
    // candidates.
    {
        int k = 0;
        ArrayList<DecreasingVectorIter> iters = new ArrayList<>(numcand);
        if (minvote >= numcand) {
            minvote = Math.max(1, numcand - 1);
        }
        for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
            // Skip "by label", obviously
            if (DBIDUtil.equal(firstid, iditer)) {
                continue;
            }
            iters.add(new DecreasingVectorIter(relation.get(iditer)));
        }
        loop: while (union_outliers < desired_outliers) {
            for (DecreasingVectorIter iter : iters) {
                if (!iter.valid()) {
                    LOG.warning("Union_outliers=" + union_outliers + " < desired_outliers=" + desired_outliers + " minvote=" + minvote);
                    break loop;
                }
                int cur = iter.dim();
                outliers_seen[cur] += 1;
                if (outliers_seen[cur] == minvote) {
                    union_outliers += 1;
                }
                iter.advance();
            }
            k++;
        }
        LOG.verbose("Merged top " + k + " outliers to: " + union_outliers + " outliers (desired: at least " + desired_outliers + ")");
    }
    // Build the final weight vector.
    final double[] estimated_weights = new double[dim];
    final double[] estimated_truth = new double[dim];
    updateEstimations(outliers_seen, union_outliers, estimated_weights, estimated_truth);
    DoubleVector estimated_truth_vec = DoubleVector.wrap(estimated_truth);
    PrimitiveDistanceFunction<NumberVector> wdist = getDistanceFunction(estimated_weights);
    PrimitiveDistanceFunction<NumberVector> tdist = wdist;
    // Build the naive ensemble:
    final double[] naiveensemble = new double[dim];
    {
        double[] buf = new double[numcand];
        for (int d = 0; d < dim; d++) {
            int i = 0;
            for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
                if (DBIDUtil.equal(firstid, iditer)) {
                    continue;
                }
                final NumberVector vec = relation.get(iditer);
                buf[i] = vec.doubleValue(d);
                i++;
            }
            naiveensemble[d] = voting.combine(buf, i);
            if (Double.isNaN(naiveensemble[d])) {
                LOG.warning("NaN after combining: " + FormatUtil.format(buf) + " i=" + i + " " + voting.toString());
            }
        }
    }
    DoubleVector naivevec = DoubleVector.wrap(naiveensemble);
    // Compute single AUC scores and estimations.
    // Remember the method most similar to the estimation
    double bestauc = 0.0;
    String bestaucstr = "";
    double bestcost = Double.POSITIVE_INFINITY;
    String bestcoststr = "";
    DBID bestid = null;
    double bestest = Double.POSITIVE_INFINITY;
    {
        final double[] greedyensemble = new double[dim];
        // Compute individual scores
        for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
            if (DBIDUtil.equal(firstid, iditer)) {
                continue;
            }
            // fout.append(labels.get(id));
            final NumberVector vec = relation.get(iditer);
            singleEnsemble(greedyensemble, vec);
            double auc = ROCEvaluation.computeROCAUC(positive, new DecreasingVectorIter(DoubleVector.wrap(greedyensemble)));
            double estimated = wdist.distance(DoubleVector.wrap(greedyensemble), estimated_truth_vec);
            double cost = tdist.distance(DoubleVector.wrap(greedyensemble), refvec);
            LOG.verbose("ROC AUC: " + auc + " estimated " + estimated + " cost " + cost + " " + labels.get(iditer));
            if (auc > bestauc) {
                bestauc = auc;
                bestaucstr = labels.get(iditer);
            }
            if (cost < bestcost) {
                bestcost = cost;
                bestcoststr = labels.get(iditer);
            }
            if (estimated < bestest || bestid == null) {
                bestest = estimated;
                bestid = DBIDUtil.deref(iditer);
            }
        }
    }
    // Initialize ensemble with "best" method
    if (prescaling != null) {
        LOG.verbose("Input prescaling: " + prescaling);
    }
    LOG.verbose("Distance function: " + wdist);
    LOG.verbose("Ensemble voting: " + voting);
    if (scaling != null) {
        LOG.verbose("Ensemble rescaling: " + scaling);
    }
    LOG.verbose("Initial estimation of outliers: " + union_outliers);
    LOG.verbose("Initializing ensemble with: " + labels.get(bestid));
    ModifiableDBIDs ensemble = DBIDUtil.newArray(bestid);
    ModifiableDBIDs enscands = DBIDUtil.newHashSet(relation.getDBIDs());
    ModifiableDBIDs dropped = DBIDUtil.newHashSet(relation.size());
    dropped.add(firstid);
    enscands.remove(bestid);
    enscands.remove(firstid);
    final double[] greedyensemble = new double[dim];
    singleEnsemble(greedyensemble, relation.get(bestid));
    // Greedily grow the ensemble
    final double[] testensemble = new double[dim];
    while (enscands.size() > 0) {
        NumberVector greedyvec = DoubleVector.wrap(greedyensemble);
        final double oldd = wdist.distance(estimated_truth_vec, greedyvec);
        final int heapsize = enscands.size();
        ModifiableDoubleDBIDList heap = DBIDUtil.newDistanceDBIDList(heapsize);
        double[] tmp = new double[dim];
        for (DBIDIter iter = enscands.iter(); iter.valid(); iter.advance()) {
            final NumberVector vec = relation.get(iter);
            singleEnsemble(tmp, vec);
            double diversity = wdist.distance(DoubleVector.wrap(greedyensemble), greedyvec);
            heap.add(diversity, iter);
        }
        heap.sort();
        for (DoubleDBIDListMIter it = heap.iter(); heap.size() > 0; it.remove()) {
            // Last
            it.seek(heap.size() - 1);
            enscands.remove(it);
            final NumberVector vec = relation.get(it);
            // Build combined ensemble.
            {
                double[] buf = new double[ensemble.size() + 1];
                for (int i = 0; i < dim; i++) {
                    int j = 0;
                    for (DBIDIter iter = ensemble.iter(); iter.valid(); iter.advance()) {
                        buf[j] = relation.get(iter).doubleValue(i);
                        j++;
                    }
                    buf[j] = vec.doubleValue(i);
                    testensemble[i] = voting.combine(buf, j + 1);
                }
            }
            applyScaling(testensemble, scaling);
            NumberVector testvec = DoubleVector.wrap(testensemble);
            double newd = wdist.distance(estimated_truth_vec, testvec);
            // labels.get(bestadd));
            if (newd < oldd) {
                System.arraycopy(testensemble, 0, greedyensemble, 0, dim);
                ensemble.add(it);
                // Recompute heap
                break;
            } else {
                dropped.add(it);
                // logger.verbose("Discarding: " + labels.get(bestadd));
                if (refine_truth) {
                    // Update target vectors and weights
                    ArrayList<DecreasingVectorIter> iters = new ArrayList<>(numcand);
                    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
                        // Skip "by label", obviously
                        if (DBIDUtil.equal(firstid, iditer) || dropped.contains(iditer)) {
                            continue;
                        }
                        iters.add(new DecreasingVectorIter(relation.get(iditer)));
                    }
                    if (minvote >= iters.size()) {
                        minvote = iters.size() - 1;
                    }
                    union_outliers = 0;
                    Arrays.fill(outliers_seen, 0);
                    while (union_outliers < desired_outliers) {
                        for (DecreasingVectorIter iter : iters) {
                            if (!iter.valid()) {
                                break;
                            }
                            int cur = iter.dim();
                            if (outliers_seen[cur] == 0) {
                                outliers_seen[cur] = 1;
                            } else {
                                outliers_seen[cur] += 1;
                            }
                            if (outliers_seen[cur] == minvote) {
                                union_outliers += 1;
                            }
                            iter.advance();
                        }
                    }
                    LOG.warning("New num outliers: " + union_outliers);
                    updateEstimations(outliers_seen, union_outliers, estimated_weights, estimated_truth);
                    estimated_truth_vec = DoubleVector.wrap(estimated_truth);
                }
            }
        }
    }
    // Build the improved ensemble:
    StringBuilder greedylbl = new StringBuilder();
    {
        for (DBIDIter iter = ensemble.iter(); iter.valid(); iter.advance()) {
            if (greedylbl.length() > 0) {
                greedylbl.append(' ');
            }
            greedylbl.append(labels.get(iter));
        }
    }
    DoubleVector greedyvec = DoubleVector.wrap(greedyensemble);
    if (refine_truth) {
        LOG.verbose("Estimated outliers remaining: " + union_outliers);
    }
    LOG.verbose("Greedy ensemble (" + ensemble.size() + "): " + greedylbl.toString());
    LOG.verbose("Best single ROC AUC: " + bestauc + " (" + bestaucstr + ")");
    LOG.verbose("Best single cost:    " + bestcost + " (" + bestcoststr + ")");
    // Evaluate the naive ensemble and the "shrunk" ensemble
    double naiveauc, naivecost;
    {
        naiveauc = ROCEvaluation.computeROCAUC(positive, new DecreasingVectorIter(naivevec));
        naivecost = tdist.distance(naivevec, refvec);
        LOG.verbose("Naive ensemble AUC:   " + naiveauc + " cost: " + naivecost);
        LOG.verbose("Naive ensemble Gain:  " + gain(naiveauc, bestauc, 1) + " cost gain: " + gain(naivecost, bestcost, 0));
    }
    double greedyauc, greedycost;
    {
        greedyauc = ROCEvaluation.computeROCAUC(positive, new DecreasingVectorIter(greedyvec));
        greedycost = tdist.distance(greedyvec, refvec);
        LOG.verbose("Greedy ensemble AUC:  " + greedyauc + " cost: " + greedycost);
        LOG.verbose("Greedy ensemble Gain to best:  " + gain(greedyauc, bestauc, 1) + " cost gain: " + gain(greedycost, bestcost, 0));
        LOG.verbose("Greedy ensemble Gain to naive: " + gain(greedyauc, naiveauc, 1) + " cost gain: " + gain(greedycost, naivecost, 0));
    }
    {
        MeanVariance meanauc = new MeanVariance();
        MeanVariance meancost = new MeanVariance();
        HashSetModifiableDBIDs candidates = DBIDUtil.newHashSet(relation.getDBIDs());
        candidates.remove(firstid);
        for (int i = 0; i < 1000; i++) {
            // Build the improved ensemble:
            final double[] randomensemble = new double[dim];
            {
                DBIDs random = DBIDUtil.randomSample(candidates, ensemble.size(), (long) i);
                double[] buf = new double[random.size()];
                for (int d = 0; d < dim; d++) {
                    int j = 0;
                    for (DBIDIter iter = random.iter(); iter.valid(); iter.advance()) {
                        assert (!DBIDUtil.equal(firstid, iter));
                        final NumberVector vec = relation.get(iter);
                        buf[j] = vec.doubleValue(d);
                        j++;
                    }
                    randomensemble[d] = voting.combine(buf, j);
                }
            }
            applyScaling(randomensemble, scaling);
            NumberVector randomvec = DoubleVector.wrap(randomensemble);
            double auc = ROCEvaluation.computeROCAUC(positive, new DecreasingVectorIter(randomvec));
            meanauc.put(auc);
            double cost = tdist.distance(randomvec, refvec);
            meancost.put(cost);
        }
        LOG.verbose("Random ensemble AUC:  " + meanauc.getMean() + " + stddev: " + meanauc.getSampleStddev() + " = " + (meanauc.getMean() + meanauc.getSampleStddev()));
        LOG.verbose("Random ensemble Gain: " + gain(meanauc.getMean(), bestauc, 1));
        LOG.verbose("Greedy improvement:   " + (greedyauc - meanauc.getMean()) / meanauc.getSampleStddev() + " standard deviations.");
        LOG.verbose("Random ensemble Cost: " + meancost.getMean() + " + stddev: " + meancost.getSampleStddev() + " = " + (meancost.getMean() + meanauc.getSampleStddev()));
        LOG.verbose("Random ensemble Gain: " + gain(meancost.getMean(), bestcost, 0));
        LOG.verbose("Greedy improvement:   " + (meancost.getMean() - greedycost) / meancost.getSampleStddev() + " standard deviations.");
        LOG.verbose("Naive ensemble Gain to random: " + gain(naiveauc, meanauc.getMean(), 1) + " cost gain: " + gain(naivecost, meancost.getMean(), 0));
        LOG.verbose("Random ensemble Gain to naive: " + gain(meanauc.getMean(), naiveauc, 1) + " cost gain: " + gain(meancost.getMean(), naivecost, 0));
        LOG.verbose("Greedy ensemble Gain to random: " + gain(greedyauc, meanauc.getMean(), 1) + " cost gain: " + gain(greedycost, meancost.getMean(), 0));
    }
}

Example 58 with AbortException

use of de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException in project elki by elki-project.

the class DistanceQuantileSampler method run.

/**
 * Run the distance quantile sampler.
 *
 * @param database
 * @param rel
 * @return Distances sample
 */
public CollectionResult<double[]> run(Database database, Relation<O> rel) {
    DistanceQuery<O> dq = rel.getDistanceQuery(getDistanceFunction());
    int size = rel.size();
    long pairs = (size * (long) size) >> 1;
    final long ssize = sampling <= 1 ? (long) Math.ceil(sampling * pairs) : (long) sampling;
    if (ssize > Integer.MAX_VALUE) {
        throw new AbortException("Sampling size too large.");
    }
    final int qsize = quantile <= 0 ? 1 : (int) Math.ceil(quantile * ssize);
    DoubleMaxHeap heap = new DoubleMaxHeap(qsize);
    ArrayDBIDs ids = DBIDUtil.ensureArray(rel.getDBIDs());
    DBIDArrayIter i1 = ids.iter(), i2 = ids.iter();
    Random r = rand.getSingleThreadedRandom();
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Sampling", (int) ssize, LOG) : null;
    for (long i = 0; i < ssize; i++) {
        int x = r.nextInt(size - 1) + 1, y = r.nextInt(x);
        double dist = dq.distance(i1.seek(x), i2.seek(y));
        // Skip NaN, and/or zeros.
        if (dist != dist || (nozeros && dist < Double.MIN_NORMAL)) {
            continue;
        }
        heap.add(dist, qsize);
        LOG.incrementProcessed(prog);
    }
    LOG.statistics(new DoubleStatistic(PREFIX + ".quantile", quantile));
    LOG.statistics(new LongStatistic(PREFIX + ".samplesize", ssize));
    LOG.statistics(new DoubleStatistic(PREFIX + ".distance", heap.peek()));
    LOG.ensureCompleted(prog);
    Collection<String> header = Arrays.asList(new String[] { "Distance" });
    Collection<double[]> data = Arrays.asList(new double[][] { new double[] { heap.peek() } });
    return new CollectionResult<double[]>("Distances sample", "distance-sample", data, header);
}

Example 59 with AbortException

use of de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException in project elki by elki-project.

the class HashmapDatabase method insert.

@Override
public DBIDs insert(ObjectBundle objpackages) {
    if (objpackages.dataLength() == 0) {
        return DBIDUtil.EMPTYDBIDS;
    }
    // insert into db
    ArrayModifiableDBIDs newids = DBIDUtil.newArray(objpackages.dataLength());
    Relation<?>[] targets = alignColumns(objpackages);
    DBIDVar var = DBIDUtil.newVar();
    for (int j = 0; j < objpackages.dataLength(); j++) {
        // insert object
        if (!objpackages.assignDBID(j, var)) {
            var.set(DBIDUtil.generateSingleDBID());
        }
        if (ids.contains(var)) {
            throw new AbortException("Duplicate DBID conflict.");
        }
        ids.add(var);
        for (int i = 0; i < targets.length; i++) {
            if (!(targets[i] instanceof ModifiableRelation)) {
                throw new AbortException("Non-modifiable relations have been added to the database.");
            }
            @SuppressWarnings("unchecked") final ModifiableRelation<Object> relation = (ModifiableRelation<Object>) targets[i];
            relation.insert(var, objpackages.data(j, i));
        }
        newids.add(var);
    }
    // fire insertion event
    eventManager.fireObjectsInserted(newids);
    return newids;
}

Example 60 with AbortException

use of de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException in project elki by elki-project.

the class LoOP method run.

/**
 * Performs the LoOP algorithm on the given database.
 *
 * @param database Database to process
 * @param relation Relation to process
 * @return Outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    StepProgress stepprog = LOG.isVerbose() ? new StepProgress(5) : null;
    Pair<KNNQuery<O>, KNNQuery<O>> pair = getKNNQueries(database, relation, stepprog);
    KNNQuery<O> knnComp = pair.getFirst();
    KNNQuery<O> knnReach = pair.getSecond();
    // Assert we got something
    if (knnComp == null) {
        throw new AbortException("No kNN queries supported by database for comparison distance function.");
    }
    if (knnReach == null) {
        throw new AbortException("No kNN queries supported by database for density estimation distance function.");
    }
    // FIXME: tie handling!
    // Probabilistic distances
    WritableDoubleDataStore pdists = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB);
    LOG.beginStep(stepprog, 3, "Computing pdists");
    computePDists(relation, knnReach, pdists);
    // Compute PLOF values.
    WritableDoubleDataStore plofs = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    LOG.beginStep(stepprog, 4, "Computing PLOF");
    double nplof = computePLOFs(relation, knnComp, pdists, plofs);
    // Normalize the outlier scores.
    DoubleMinMax mm = new DoubleMinMax();
    {
        // compute LOOP_SCORE of each db object
        LOG.beginStep(stepprog, 5, "Computing LoOP scores");
        FiniteProgress progressLOOPs = LOG.isVerbose() ? new FiniteProgress("LoOP for objects", relation.size(), LOG) : null;
        final double norm = 1. / (nplof * MathUtil.SQRT2);
        for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
            double loop = NormalDistribution.erf((plofs.doubleValue(iditer) - 1.) * norm);
            plofs.putDouble(iditer, loop);
            mm.put(loop);
            LOG.incrementProcessed(progressLOOPs);
        }
        LOG.ensureCompleted(progressLOOPs);
    }
    LOG.setCompleted(stepprog);
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Local Outlier Probabilities", "loop-outlier", plofs, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new ProbabilisticOutlierScore(mm.getMin(), mm.getMax(), 0.);
    return new OutlierResult(scoreMeta, scoreResult);
}