/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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.clustering.clusterer;
import java.util.ArrayList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Tools;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.clustering.CentroidClusterModel;
import com.rapidminer.operator.clustering.ClusterModel;
import com.rapidminer.operator.learner.CapabilityProvider;
import com.rapidminer.operator.ports.metadata.CapabilityPrecondition;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.RandomGenerator;
import com.rapidminer.tools.math.similarity.DistanceMeasure;
import com.rapidminer.tools.math.similarity.DistanceMeasureHelper;
import com.rapidminer.tools.math.similarity.DistanceMeasures;
import de.dfki.madm.operator.KMeanspp;
/**
* This operator represents an implementation of k-means. This operator will create a cluster
* attribute if not present yet.
*
* @author Sebastian Land
*/
public class KMeans extends RMAbstractClusterer implements CapabilityProvider {
/** The parameter name for "the maximal number of clusters" */
public static final String PARAMETER_K = "k";
private DistanceMeasureHelper measureHelper = new DistanceMeasureHelper(this);
private DistanceMeasure presetMeasure = null;
/**
* The parameter name for "the maximal number of runs of the k method with random
* initialization that are performed"
*/
public static final String PARAMETER_MAX_RUNS = "max_runs";
/**
* The parameter name for "the maximal number of iterations performed for one run of the k
* method"
*/
public static final String PARAMETER_MAX_OPTIMIZATION_STEPS = "max_optimization_steps";
public KMeans(OperatorDescription description) {
super(description);
getExampleSetInputPort().addPrecondition(new CapabilityPrecondition(this, getExampleSetInputPort()));
}
/**
* Overrides the measure specified by the operator parameters. If set to null, parameters will
* be used again to determine the measure.
*/
public void setPresetMeasure(DistanceMeasure me) {
this.presetMeasure = me;
}
@Override
public ClusterModel generateClusterModel(ExampleSet exampleSet) throws OperatorException {
int k = getParameterAsInt(PARAMETER_K);
int maxOptimizationSteps = getParameterAsInt(PARAMETER_MAX_OPTIMIZATION_STEPS);
int maxRuns = getParameterAsInt(PARAMETER_MAX_RUNS);
boolean kpp = getParameterAsBoolean(KMeanspp.PARAMETER_USE_KPP);
boolean addAsLabel = getParameterAsBoolean(RMAbstractClusterer.PARAMETER_ADD_AS_LABEL);
boolean removeUnlabeled = getParameterAsBoolean(RMAbstractClusterer.PARAMETER_REMOVE_UNLABELED);
// init operator progress
getProgress().setTotal(maxRuns * maxOptimizationSteps);
DistanceMeasure measure;
if (presetMeasure != null) {
measure = presetMeasure;
measure.init(exampleSet);
} else {
measure = measureHelper.getInitializedMeasure(exampleSet);
}
// checking and creating ids if necessary
Tools.checkAndCreateIds(exampleSet);
// additional checks
Tools.onlyNonMissingValues(exampleSet, getOperatorClassName(), this, new String[0]);
if (exampleSet.size() < k) {
throw new UserError(this, 142, k);
}
// extracting attribute names
Attributes attributes = exampleSet.getAttributes();
ArrayList<String> attributeNames = new ArrayList<>(attributes.size());
for (Attribute attribute : attributes) {
attributeNames.add(attribute.getName());
}
RandomGenerator generator = RandomGenerator.getRandomGenerator(this);
double minimalIntraClusterDistance = Double.POSITIVE_INFINITY;
CentroidClusterModel bestModel = null;
int[] bestAssignments = null;
double[] values = new double[attributes.size()];
for (int iter = 0; iter < maxRuns; iter++) {
CentroidClusterModel model = new CentroidClusterModel(exampleSet, k, attributeNames, measure, addAsLabel,
removeUnlabeled);
// init centroids by assigning one single, unique example!
int i = 0;
if (kpp) {
KMeanspp kmpp = new KMeanspp(getOperatorDescription(), k, exampleSet, measure, generator);
int[] hilf = kmpp.getStart();
int i1 = 0;
for (int id : hilf) {
double[] as = getAsDoubleArray(exampleSet.getExample(id), attributes, values);
model.assignExample(i1, as);
i1++;
}
} else {
for (Integer index : generator.nextIntSetWithRange(0, exampleSet.size(), k)) {
model.assignExample(i, getAsDoubleArray(exampleSet.getExample(index), attributes, values));
i++;
}
}
model.finishAssign();
// run optimization steps
int[] centroidAssignments = new int[exampleSet.size()];
boolean stable = false;
for (int step = 0; step < maxOptimizationSteps && !stable; step++) {
getProgress().step();
// assign examples to new centroids
i = 0;
for (Example example : exampleSet) {
double[] exampleValues = getAsDoubleArray(example, attributes, values);
double nearestDistance = measure.calculateDistance(model.getCentroidCoordinates(0), exampleValues);
int nearestIndex = 0;
for (int centroidIndex = 1; centroidIndex < k; centroidIndex++) {
double distance = measure.calculateDistance(model.getCentroidCoordinates(centroidIndex),
exampleValues);
if (distance < nearestDistance) {
nearestDistance = distance;
nearestIndex = centroidIndex;
}
}
centroidAssignments[i] = nearestIndex;
model.getCentroid(nearestIndex).assignExample(exampleValues);
i++;
}
// finishing assignment
stable = model.finishAssign();
}
// assessing quality of this model
double distanceSum = 0;
i = 0;
for (Example example : exampleSet) {
double distance = measure.calculateDistance(model.getCentroidCoordinates(centroidAssignments[i]),
getAsDoubleArray(example, attributes, values));
distanceSum += distance * distance;
i++;
}
if (distanceSum < minimalIntraClusterDistance) {
bestModel = model;
minimalIntraClusterDistance = distanceSum;
bestAssignments = centroidAssignments;
}
getProgress().setCompleted((iter + 1) * maxOptimizationSteps);
}
bestModel.setClusterAssignments(bestAssignments, exampleSet);
if (addsClusterAttribute()) {
Attribute cluster;
if (!getParameterAsBoolean(PARAMETER_ADD_AS_LABEL)) {
cluster = AttributeFactory.createAttribute(Attributes.CLUSTER_NAME, Ontology.NOMINAL);
exampleSet.getExampleTable().addAttribute(cluster);
attributes.setCluster(cluster);
} else {
cluster = AttributeFactory.createAttribute(Attributes.LABEL_NAME, Ontology.NOMINAL);
exampleSet.getExampleTable().addAttribute(cluster);
attributes.setLabel(cluster);
}
int i = 0;
for (Example example : exampleSet) {
example.setValue(cluster, "cluster_" + bestAssignments[i]);
i++;
}
}
getProgress().complete();
return bestModel;
}
private double[] getAsDoubleArray(Example example, Attributes attributes, double[] values) {
int i = 0;
for (Attribute attribute : attributes) {
values[i] = example.getValue(attribute);
i++;
}
return values;
}
@Override
public Class<? extends ClusterModel> getClusterModelClass() {
return CentroidClusterModel.class;
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
boolean supportsNominal = false;
boolean pureNominal = false;
try {
// Check if a measure type is selected that supports nominal attributes
int selectedMeasureType = measureHelper.getSelectedMeasureType();
pureNominal = selectedMeasureType == DistanceMeasures.NOMINAL_MEASURES_TYPE;
supportsNominal = selectedMeasureType == DistanceMeasures.MIXED_MEASURES_TYPE || pureNominal;
} catch (UndefinedParameterError e) {
// parameter is undefined we will stick tell that we do not support nominal attributes
}
switch (capability) {
case NUMERICAL_ATTRIBUTES:
return !pureNominal;
case BINOMINAL_ATTRIBUTES:
case POLYNOMINAL_ATTRIBUTES:
return supportsNominal;
default:
return true;
}
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeInt(PARAMETER_K, "The number of clusters which should be detected.", 2, Integer.MAX_VALUE,
2, false));
types.add(new ParameterTypeInt(PARAMETER_MAX_RUNS,
"The maximal number of runs of k-Means with random initialization that are performed.", 1, Integer.MAX_VALUE,
10, false));
ParameterType type = new ParameterTypeBoolean(KMeanspp.PARAMETER_USE_KPP, KMeanspp.SHORT_DESCRIPTION, false);
type.setExpert(false);
types.add(type);
for (ParameterType a : DistanceMeasures.getParameterTypes(this)) {
if (a.getKey() == DistanceMeasures.PARAMETER_MEASURE_TYPES) {
a.setDefaultValue(DistanceMeasures.DIVERGENCES_TYPE);
}
if (a.getKey() == DistanceMeasures.PARAMETER_DIVERGENCE) {
a.setDefaultValue(6);
}
types.add(a);
}
types.add(new ParameterTypeInt(PARAMETER_MAX_OPTIMIZATION_STEPS,
"The maximal number of iterations performed for one run of k-Means.", 1, Integer.MAX_VALUE, 100, false));
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
}