/*
* RapidMiner
*
* Copyright (C) 2001-2011 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* 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/.
*/
package com.rapidminer.operator.validation.clustering;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.ValueDouble;
import com.rapidminer.operator.clustering.ClusterModel;
import com.rapidminer.operator.performance.EstimatedPerformance;
import com.rapidminer.operator.performance.PerformanceCriterion;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.PassThroughOrGenerateRule;
import com.rapidminer.operator.similarity.SimilarityMeasureObject;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.math.similarity.DistanceMeasure;
/**
* This operator is used to evaluate a non-hierarchical cluster model based on the
* average within cluster similarity/distance. It is computed by averaging all
* similarities / distances between each pair of examples of a cluster.
*
* @author Michael Wurst, Ingo Mierswa, Sebastian Land
*/
public class ClusterDensityEvaluator extends Operator {
private double avgClusterSim = 0.0;
private InputPort exampleSetInput = getInputPorts().createPort("example set");
private InputPort distanceInput = getInputPorts().createPort("distance measure", SimilarityMeasureObject.class);
private InputPort performanceInput = getInputPorts().createPort("performance vector");
private InputPort clusterModelInput = getInputPorts().createPort("cluster model", ClusterModel.class);
private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
private OutputPort performanceOutput = getOutputPorts().createPort("performance vector");
/**
* Constructor for ClusterDensityEvaluator.
*/
public ClusterDensityEvaluator(OperatorDescription description) {
super(description);
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, Ontology.ATTRIBUTE_VALUE, Attributes.ID_NAME));
getTransformer().addPassThroughRule(exampleSetInput, exampleSetOutput);
getTransformer().addRule(new PassThroughOrGenerateRule(performanceInput, performanceOutput, new MetaData(PerformanceVector.class)));
addValue(new ValueDouble("clusterdensity", "Avg. within cluster similarity/distance", false) {
@Override
public double getDoubleValue() {
return avgClusterSim;
}
});
}
@Override
public void doWork() throws OperatorException {
SimilarityMeasureObject simMeasure = distanceInput.getData();
DistanceMeasure measure = simMeasure.getDistanceMeasure();
ExampleSet exampleSet = exampleSetInput.getData();
ClusterModel clusterModel = clusterModelInput.getData();
PerformanceVector performance = performanceInput.getDataOrNull();
if (performance == null)
performance = new PerformanceVector();
double[] avgWithinClusterSims = withinClusterAvgSim(clusterModel, exampleSet, measure);
avgClusterSim = avgWithinClusterSims[clusterModel.getNumberOfClusters()];
PerformanceCriterion withinClusterSim = null;
if (measure.isDistance())
withinClusterSim = new EstimatedPerformance("Avg. within cluster distance", avgClusterSim, 1, true);
else
withinClusterSim = new EstimatedPerformance("Avg. within cluster similarity", avgClusterSim, 1, false);
performance.addCriterion(withinClusterSim);
for (int i = 0; i < clusterModel.getNumberOfClusters(); i++) {
PerformanceCriterion withinSingleClusterSim = null;
if (measure.isDistance())
withinSingleClusterSim = new EstimatedPerformance("Avg. within cluster distance for cluster " + clusterModel.getCluster(i).getClusterId(), avgWithinClusterSims[i], 1, true);
else
withinSingleClusterSim = new EstimatedPerformance("Avg. within cluster similarity for cluster " + clusterModel.getCluster(i).getClusterId(), avgWithinClusterSims[i], 1, false);
performance.addCriterion(withinSingleClusterSim);
}
exampleSetOutput.deliver(exampleSet);
performanceOutput.deliver(performance);
}
private double[] withinClusterAvgSim(ClusterModel clusterModel, ExampleSet exampleSet, DistanceMeasure measure) {
Attribute id = exampleSet.getAttributes().getId();
double[] result = new double[clusterModel.getNumberOfClusters() + 1];
for (Example example: exampleSet) {
int clusterIndex = id.isNominal() ? clusterModel.getClusterIndexOfId(example.getValueAsString(id)) : clusterModel.getClusterIndexOfId(example.getValue(id));
for (Example compExample: exampleSet) {
int compClusterIndex = id.isNominal() ? clusterModel.getClusterIndexOfId(compExample.getValueAsString(id)) : clusterModel.getClusterIndexOfId(compExample.getValue(id));
if (clusterIndex == compClusterIndex) {
double v = measure.calculateSimilarity(example, compExample);
result[clusterIndex] += v;
}
}
}
double sum = 0.0;
int totalCount = 0;
for (int i = 0; i < clusterModel.getNumberOfClusters(); i++) {
sum += result[i];
int clusterSize = clusterModel.getCluster(i).getNumberOfExamples();
result[i] = result[i] /= clusterSize;
totalCount += clusterSize;;
}
result[clusterModel.getNumberOfClusters()] = sum / totalCount;
return result;
}
}