/*
* RapidMiner
*
* Copyright (C) 2001-2008 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.learner.clustering.clusterer;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.clustering.ClusterModel;
import com.rapidminer.operator.learner.clustering.IdUtils;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.Kernel;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelNeural;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial;
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelRadial;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
/**
* An implementation of Support Vector Clustering based on {@rapidminer.cite BenHur/etal/2001a}.
*
* @author Stefan Rueping, Ingo Mierswa, Michael Wurst
* @version $Id: SVClusteringOperator.java,v 1.10 2008/09/12 10:31:39 tobiasmalbrecht Exp $
*/
public class SVClusteringOperator extends AbstractDensityBasedClusterer {
/** The parameter name for "The SVM kernel type" */
public static final String PARAMETER_KERNEL_TYPE = "kernel_type";
/** The parameter name for "The SVM kernel parameter gamma (radial)." */
public static final String PARAMETER_KERNEL_GAMMA = "kernel_gamma";
/** The parameter name for "The SVM kernel parameter degree (polynomial)." */
public static final String PARAMETER_KERNEL_DEGREE = "kernel_degree";
/** The parameter name for "The SVM kernel parameter a (neural)." */
public static final String PARAMETER_KERNEL_A = "kernel_a";
/** The parameter name for "The SVM kernel parameter b (neural)." */
public static final String PARAMETER_KERNEL_B = "kernel_b";
/** The parameter name for "Size of the cache for kernel evaluations im MB " */
public static final String PARAMETER_KERNEL_CACHE = "kernel_cache";
/** The parameter name for "Precision on the KKT conditions" */
public static final String PARAMETER_CONVERGENCE_EPSILON = "convergence_epsilon";
/** The parameter name for "Stop after this many iterations" */
public static final String PARAMETER_MAX_ITERATIONS = "max_iterations";
/** The parameter name for "The fraction of allowed outliers." */
public static final String PARAMETER_P = "p";
/** The parameter name for "Use this radius instead of the calculated one (-1 for calculated radius)." */
public static final String PARAMETER_R = "r";
/** The parameter name for "The number of virtual sample points to check for neighborship." */
public static final String PARAMETER_NUMBER_SAMPLE_POINTS = "number_sample_points";
/** The kernels which can be used from RapidMiner for the mySVM / myKLR. */
private static final String[] KERNEL_TYPES = {
"dot", "radial", "polynomial", "neural"
};
/** Indicates a linear kernel. */
public static final int KERNEL_DOT = 0;
/** Indicates a rbf kernel. */
public static final int KERNEL_RADIAL = 1;
/** Indicates a polynomial kernel. */
public static final int KERNEL_POLYNOMIAL = 2;
/** Indicates a neural net kernel. */
public static final int KERNEL_NEURAL = 3;
private SVClustering model;
public SVClusteringOperator(OperatorDescription description) {
super(description);
}
public ClusterModel createClusterModel(ExampleSet es) throws OperatorException {
es.remapIds();
// kernel
int kernelType = getParameterAsInt(PARAMETER_KERNEL_TYPE);
int cacheSize = getParameterAsInt(PARAMETER_KERNEL_CACHE);
Kernel kernel = createKernel(kernelType);
if (kernelType == KERNEL_RADIAL)
((KernelRadial) kernel).setGamma(getParameterAsDouble(PARAMETER_KERNEL_GAMMA));
else if (kernelType == KERNEL_POLYNOMIAL)
((KernelPolynomial) kernel).setDegree(getParameterAsInt(PARAMETER_KERNEL_DEGREE));
else if (kernelType == KERNEL_NEURAL)
((KernelNeural) kernel).setParameters(getParameterAsDouble(PARAMETER_KERNEL_A), getParameterAsDouble(PARAMETER_KERNEL_B));
SVCExampleSet svmExamples = new SVCExampleSet(es, false);
kernel.init(svmExamples, cacheSize);
model = new SVClustering(this, kernel, svmExamples);
model.train();
ClusterModel result = doClustering(es);
return result;
}
protected List<String> getNeighbours(ExampleSet es, String id) throws UndefinedParameterError {
List ids = getIds();
List<String> neighbors = new LinkedList<String>();
Example example = getExample(es, id);
double paramR = getParameterAsDouble(PARAMETER_R);
double maxRadius = paramR < 0 ? model.getR() : paramR;
int numSamplePoints = getParameterAsInt(PARAMETER_NUMBER_SAMPLE_POINTS);
Iterator it = ids.iterator();
while (it.hasNext()) {
String neighborId = (String) it.next();
if ((!id.equals(neighborId)) && (getAssignment(neighborId) == UNASSIGNED)) {
Example neighbor = getExample(es, neighborId);
double[] directions = new double[example.getAttributes().size()];
int x = 0;
for (Attribute attribute : example.getAttributes()) {
directions[x++] = neighbor.getValue(attribute) - example.getValue(attribute);
}
boolean addAsNeighbor = true;
for (int i = 0; i < numSamplePoints; i++) {
if (addAsNeighbor) {
double[] virtualExample = new double[directions.length];
x = 0;
for (Attribute attribute : example.getAttributes()) {
virtualExample[x] = example.getValue(attribute) + (i + 1) * directions[x] / (numSamplePoints + 1);
x++;
}
com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExample svmExample = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExample(virtualExample);
double currentRadius = model.predict(svmExample);
if (currentRadius > maxRadius) {
addAsNeighbor = false;
break;
}
} else {
break;
}
}
if (addAsNeighbor)
neighbors.add(neighborId);
}
}
return neighbors;
}
/**
* Creates a new kernel of the given type. The kernel type has to be one out of KERNEL_DOT, KERNEL_RADIAL, KERNEL_POLYNOMIAL, or KERNEL_NEURAL.
*/
public static Kernel createKernel(int kernelType) {
switch (kernelType) {
case KERNEL_RADIAL:
return new KernelRadial();
case KERNEL_POLYNOMIAL:
return new KernelPolynomial();
case KERNEL_NEURAL:
return new KernelNeural();
default:
return new KernelDot();
}
}
private Example getExample(ExampleSet es, String id) {
return IdUtils.getExampleFromId(es, id);
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeCategory(PARAMETER_KERNEL_TYPE, "The SVM kernel type", KERNEL_TYPES, KERNEL_RADIAL);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_GAMMA, "The SVM kernel parameter gamma (radial).", 0.0d, Double.POSITIVE_INFINITY, 1.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_KERNEL_DEGREE, "The SVM kernel parameter degree (polynomial).", 0, Integer.MAX_VALUE, 2);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_A, "The SVM kernel parameter a (neural).", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 1.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_B, "The SVM kernel parameter b (neural).", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0d);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeInt(PARAMETER_KERNEL_CACHE, "Size of the cache for kernel evaluations im MB ", 0, Integer.MAX_VALUE, 200));
type = new ParameterTypeDouble(PARAMETER_CONVERGENCE_EPSILON, "Precision on the KKT conditions", 0.0d, Double.POSITIVE_INFINITY, 1e-3);
types.add(type);
types.add(new ParameterTypeInt(PARAMETER_MAX_ITERATIONS, "Stop after this many iterations", 1, Integer.MAX_VALUE, 100000));
type = new ParameterTypeDouble(PARAMETER_P, "The fraction of allowed outliers.", 0, 1, 0.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_R, "Use this radius instead of the calculated one (-1 for calculated radius).", -1.0d,
Double.POSITIVE_INFINITY, -1.0d);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeInt(PARAMETER_NUMBER_SAMPLE_POINTS, "The number of virtual sample points to check for neighborship.", 1,
Integer.MAX_VALUE, 20));
return types;
}
}