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

Example 66 with OutlierResult

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

the class OutlierROCCurve method processNewResult.

@Override
public void processNewResult(ResultHierarchy hier, Result result) {
    Database db = ResultUtil.findDatabase(hier);
    // Prepare
    SetDBIDs positiveids = DBIDUtil.ensureSet(DatabaseUtil.getObjectsByLabelMatch(db, positiveClassName));
    if (positiveids.size() == 0) {
        LOG.warning("Computing a ROC curve failed - no objects matched.");
        return;
    }
    boolean nonefound = true;
    List<OutlierResult> oresults = OutlierResult.getOutlierResults(result);
    List<OrderingResult> orderings = ResultUtil.getOrderingResults(result);
    // Outlier results are the main use case.
    for (OutlierResult o : oresults) {
        ROCResult rocres = computeROCResult(o.getScores().size(), positiveids, o);
        db.getHierarchy().add(o, rocres);
        EvaluationResult ev = EvaluationResult.findOrCreate(db.getHierarchy(), o, "Evaluation of ranking", "ranking-evaluation");
        MeasurementGroup g = ev.findOrCreateGroup("Evaluation measures");
        if (!g.hasMeasure(ROCAUC_LABEL)) {
            g.addMeasure(ROCAUC_LABEL, rocres.auc, 0., 1., false);
        }
        // Process each ordering only once.
        orderings.remove(o.getOrdering());
        nonefound = false;
    }
    // otherwise apply an ordering to the database IDs.
    for (OrderingResult or : orderings) {
        DBIDs sorted = or.order(or.getDBIDs());
        ROCResult rocres = computeROCResult(or.getDBIDs().size(), positiveids, sorted);
        db.getHierarchy().add(or, rocres);
        EvaluationResult ev = EvaluationResult.findOrCreate(db.getHierarchy(), or, "Evaluation of ranking", "ranking-evaluation");
        MeasurementGroup g = ev.findOrCreateGroup("Evaluation measures");
        if (!g.hasMeasure(ROCAUC_LABEL)) {
            g.addMeasure(ROCAUC_LABEL, rocres.auc, 0., 1., false);
        }
        nonefound = false;
    }
    if (nonefound) {
        return;
    // logger.warning("No results found to process with ROC curve analyzer.
    // Got "+iterables.size()+" iterables, "+orderings.size()+" orderings.");
    }
}

Example 67 with OutlierResult

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

the class OutlierSmROCCurve method processNewResult.

@Override
public void processNewResult(ResultHierarchy hier, Result result) {
    Database db = ResultUtil.findDatabase(hier);
    // Prepare
    SetDBIDs positiveids = DBIDUtil.ensureSet(DatabaseUtil.getObjectsByLabelMatch(db, positiveClassName));
    if (positiveids.size() == 0) {
        LOG.warning("Computing a ROC curve failed - no objects matched.");
        return;
    }
    List<OutlierResult> oresults = OutlierResult.getOutlierResults(result);
    List<OrderingResult> orderings = ResultUtil.getOrderingResults(result);
    for (OutlierResult o : oresults) {
        db.getHierarchy().add(o, computeSmROCResult(positiveids, o));
        orderings.remove(o.getOrdering());
    }
}

Example 68 with OutlierResult

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

the class SimpleCOP method run.

public OutlierResult run(Database database, Relation<V> data) throws IllegalStateException {
    KNNQuery<V> knnQuery = QueryUtil.getKNNQuery(data, getDistanceFunction(), k + 1);
    DBIDs ids = data.getDBIDs();
    WritableDoubleDataStore cop_score = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
    WritableDataStore<double[]> cop_err_v = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, double[].class);
    WritableDataStore<double[][]> cop_datav = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, double[][].class);
    WritableIntegerDataStore cop_dim = DataStoreUtil.makeIntegerStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, -1);
    WritableDataStore<CorrelationAnalysisSolution<?>> cop_sol = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, CorrelationAnalysisSolution.class);
    {
        // compute neighbors of each db object
        FiniteProgress progressLocalPCA = LOG.isVerbose() ? new FiniteProgress("Correlation Outlier Probabilities", data.size(), LOG) : null;
        double sqrt2 = MathUtil.SQRT2;
        for (DBIDIter id = data.iterDBIDs(); id.valid(); id.advance()) {
            KNNList neighbors = knnQuery.getKNNForDBID(id, k + 1);
            ModifiableDBIDs nids = DBIDUtil.newArray(neighbors);
            nids.remove(id);
            // TODO: do we want to use the query point as centroid?
            CorrelationAnalysisSolution<V> depsol = dependencyDerivator.generateModel(data, nids);
            double stddev = depsol.getStandardDeviation();
            double distance = depsol.distance(data.get(id));
            double prob = NormalDistribution.erf(distance / (stddev * sqrt2));
            cop_score.putDouble(id, prob);
            cop_err_v.put(id, times(depsol.errorVector(data.get(id)), -1));
            double[][] datav = depsol.dataProjections(data.get(id));
            cop_datav.put(id, datav);
            cop_dim.putInt(id, depsol.getCorrelationDimensionality());
            cop_sol.put(id, depsol);
            LOG.incrementProcessed(progressLocalPCA);
        }
        LOG.ensureCompleted(progressLocalPCA);
    }
    // combine results.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Original Correlation Outlier Probabilities", "origcop-outlier", cop_score, ids);
    OutlierScoreMeta scoreMeta = new ProbabilisticOutlierScore();
    OutlierResult result = new OutlierResult(scoreMeta, scoreResult);
    // extra results
    result.addChildResult(new MaterializedRelation<>("Local Dimensionality", COP.COP_DIM, TypeUtil.INTEGER, cop_dim, ids));
    result.addChildResult(new MaterializedRelation<>("Error vectors", COP.COP_ERRORVEC, TypeUtil.DOUBLE_ARRAY, cop_err_v, ids));
    result.addChildResult(new MaterializedRelation<>("Data vectors", "cop-datavec", TypeUtil.MATRIX, cop_datav, ids));
    result.addChildResult(new MaterializedRelation<>("Correlation analysis", "cop-sol", new SimpleTypeInformation<CorrelationAnalysisSolution<?>>(CorrelationAnalysisSolution.class), cop_sol, ids));
    return result;
}

Example 69 with OutlierResult

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

the class ComputeSimilarityMatrixImage method processNewResult.

@Override
public void processNewResult(ResultHierarchy hier, Result result) {
    Database db = ResultUtil.findDatabase(hier);
    boolean nonefound = true;
    List<OutlierResult> oresults = OutlierResult.getOutlierResults(result);
    List<OrderingResult> orderings = ResultUtil.getOrderingResults(result);
    // Outlier results are the main use case.
    for (OutlierResult o : oresults) {
        final OrderingResult or = o.getOrdering();
        Relation<O> relation = db.getRelation(distanceFunction.getInputTypeRestriction());
        db.getHierarchy().add(or, computeSimilarityMatrixImage(relation, or.order(relation.getDBIDs()).iter()));
        // Process them only once.
        orderings.remove(or);
        nonefound = false;
    }
    // otherwise apply an ordering to the database IDs.
    for (OrderingResult or : orderings) {
        Relation<O> relation = db.getRelation(distanceFunction.getInputTypeRestriction());
        DBIDIter iter = or.order(relation.getDBIDs()).iter();
        db.getHierarchy().add(or, computeSimilarityMatrixImage(relation, iter));
        nonefound = false;
    }
    if (nonefound) {
        // Use the database ordering.
        // But be careful to NOT cause a loop, process new databases only.
        List<Database> iter = ResultUtil.filterResults(hier, Database.class);
        for (Database database : iter) {
            // Get an arbitrary representation
            Relation<O> relation = database.getRelation(distanceFunction.getInputTypeRestriction());
            db.getHierarchy().add(db, computeSimilarityMatrixImage(relation, relation.iterDBIDs()));
        }
    }
}

Example 70 with OutlierResult

use of de.lmu.ifi.dbs.elki.result.outlier.OutlierResult 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);
        }
    }
}