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package de.lmu.ifi.dbs.elki.evaluation.clustering.extractor;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
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 java.util.ArrayList;
import de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.PointerDensityHierarchyRepresentationResult;
import de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.PointerHierarchyRepresentationResult;
import de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.model.DendrogramModel;
import de.lmu.ifi.dbs.elki.database.datastore.DBIDDataStore;
import de.lmu.ifi.dbs.elki.database.datastore.DoubleDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.evaluation.Evaluator;
import de.lmu.ifi.dbs.elki.evaluation.clustering.extractor.ExtractFlatClusteringFromHierarchyEvaluator.DummyHierarchicalClusteringAlgorithm;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.result.ResultHierarchy;
import de.lmu.ifi.dbs.elki.result.ResultUtil;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ChainedParameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.workflow.AlgorithmStep;
/**
* Extract clusters from a hierarchical clustering, during the evaluation phase.
*
* Usually, it is more elegant to use {@link HDBSCANHierarchyExtraction} as
* primary algorithm. But in order to extract <em>multiple</em> partitionings
* from the same clustering, this can be useful.
*
* @author Erich Schubert
*/
public class HDBSCANHierarchyExtractionEvaluator implements Evaluator {
/**
* Class to perform the cluster extraction.
*/
private HDBSCANHierarchyExtraction inner;
/**
* Constructor.
*
* @param inner Inner algorithm instance.
*/
public HDBSCANHierarchyExtractionEvaluator(HDBSCANHierarchyExtraction inner) {
this.inner = inner;
}
@Override
public void processNewResult(ResultHierarchy hier, Result newResult) {
ArrayList<PointerHierarchyRepresentationResult> hrs = ResultUtil.filterResults(hier, newResult, PointerHierarchyRepresentationResult.class);
for(PointerHierarchyRepresentationResult pointerresult : hrs) {
DBIDs ids = pointerresult.getDBIDs();
DBIDDataStore pi = pointerresult.getParentStore();
DoubleDataStore lambda = pointerresult.getParentDistanceStore();
DoubleDataStore coredist = null;
if(pointerresult instanceof PointerDensityHierarchyRepresentationResult) {
coredist = ((PointerDensityHierarchyRepresentationResult) pointerresult).getCoreDistanceStore();
}
Clustering<DendrogramModel> result = inner.extractClusters(ids, pi, lambda, coredist);
pointerresult.addChildResult(result);
}
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
/**
* Inner algorithm to extract a clustering.
*/
HDBSCANHierarchyExtraction inner;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
ListParameterization overrides = new ListParameterization();
overrides.addParameter(AlgorithmStep.Parameterizer.ALGORITHM_ID, DummyHierarchicalClusteringAlgorithm.class);
ChainedParameterization list = new ChainedParameterization(overrides, config);
inner = ClassGenericsUtil.parameterizeOrAbort(HDBSCANHierarchyExtraction.class, list);
}
@Override
protected HDBSCANHierarchyExtractionEvaluator makeInstance() {
return new HDBSCANHierarchyExtractionEvaluator(inner);
}
}
}
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