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package de.lmu.ifi.dbs.elki.algorithm.outlier;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2013
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.CorrelationAnalysisSolution;
import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.QueryUtil;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDataStore;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
import de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.NumberDistance;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner;
import de.lmu.ifi.dbs.elki.math.statistics.distribution.NormalDistribution;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
import de.lmu.ifi.dbs.elki.result.outlier.ProbabilisticOutlierScore;
import de.lmu.ifi.dbs.elki.utilities.FormatUtil;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
/**
* Algorithm to compute local correlation outlier probability.
*
* This is the simpler, original version of COP, as published in
* <p>
* Arthur Zimek<br />
* Correlation Clustering.<br />
* PhD thesis, Chapter 18
* </p>
* which has then been refined to the method published as {@link COP}
*
* @author Erich Schubert
* @param <V> the type of NumberVector handled by this Algorithm
*/
@Title("Simple COP: Correlation Outlier Probability")
@Reference(authors = "Arthur Zimek", title = "Correlation Clustering. PhD thesis, Chapter 18", booktitle = "")
public class SimpleCOP<V extends NumberVector<?>, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm<V, D, OutlierResult> implements OutlierAlgorithm {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(SimpleCOP.class);
/**
* Number of neighbors to be considered.
*/
int k;
/**
* Holds the object performing the dependency derivation
*/
private DependencyDerivator<V, D> dependencyDerivator;
/**
* Constructor.
*
* @param distanceFunction Distance function
* @param k k Parameter
* @param pca PCA runner-
*/
public SimpleCOP(DistanceFunction<? super V, D> distanceFunction, int k, PCAFilteredRunner<V> pca) {
super(distanceFunction);
this.k = k;
this.dependencyDerivator = new DependencyDerivator<>(null, FormatUtil.NF, pca, 0, false);
}
public OutlierResult run(Database database, Relation<V> data) throws IllegalStateException {
KNNQuery<V, D> knnQuery = QueryUtil.getKNNQuery(data, getDistanceFunction(), k + 1);
DBIDs ids = data.getDBIDs();
WritableDoubleDataStore cop_score = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
WritableDataStore<Vector> cop_err_v = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, Vector.class);
WritableDataStore<Matrix> cop_datav = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, Matrix.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 = Math.sqrt(2.0);
for(DBIDIter id = data.iterDBIDs(); id.valid(); id.advance()) {
KNNList<D> 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);
Vector errv = depsol.errorVector(data.get(id)).timesEquals(-1);
cop_err_v.put(id, errv);
Matrix datav = depsol.dataProjections(data.get(id));
cop_datav.put(id, datav);
cop_dim.putInt(id, depsol.getCorrelationDimensionality());
cop_sol.put(id, depsol);
if(progressLocalPCA != null) {
progressLocalPCA.incrementProcessed(LOG);
}
}
if(progressLocalPCA != null) {
progressLocalPCA.ensureCompleted(LOG);
}
}
// combine results.
Relation<Double> scoreResult = new MaterializedRelation<>("Original Correlation Outlier Probabilities", "origcop-outlier", TypeUtil.DOUBLE, 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.VECTOR, 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;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(TypeUtil.NUMBER_VECTOR_FIELD);
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V extends NumberVector<?>, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm.Parameterizer<V, D> {
/**
* Parameter to specify the number of nearest neighbors of an object to be
* considered for computing its COP_SCORE, must be an integer greater than
* 0.
* <p/>
* Key: {@code -cop.k}
* </p>
*/
public static final OptionID K_ID = new OptionID("cop.k", "The number of nearest neighbors of an object to be considered for computing its COP_SCORE.");
/**
* Parameter for the PCA runner class.
*
* <p>
* Key: {@code -cop.pcarunner}
* </p>
*/
public static final OptionID PCARUNNER_ID = new OptionID("cop.pcarunner", "The class to compute (filtered) PCA.");
/**
* Number of neighbors to be considered.
*/
int k;
/**
* Holds the object performing the dependency derivation
*/
protected PCAFilteredRunner<V> pca;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
IntParameter kP = new IntParameter(K_ID);
kP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
if(config.grab(kP)) {
k = kP.intValue();
}
ObjectParameter<PCAFilteredRunner<V>> pcaP = new ObjectParameter<>(PCARUNNER_ID, PCAFilteredRunner.class, PCAFilteredRunner.class);
if(config.grab(pcaP)) {
pca = pcaP.instantiateClass(config);
}
}
@Override
protected SimpleCOP<V, D> makeInstance() {
return new SimpleCOP<>(distanceFunction, k, pca);
}
}
}
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