/*
* 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.features.construction;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Tools;
import com.rapidminer.example.set.AttributeWeightedExampleSet;
import com.rapidminer.generator.AttributePeak;
import com.rapidminer.generator.FeatureGenerator;
import com.rapidminer.generator.GenerationException;
import com.rapidminer.generator.SinusFactory;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.features.Individual;
import com.rapidminer.operator.features.IndividualOperator;
import com.rapidminer.tools.RandomGenerator;
/**
* The mutation operator for FourierGGA. This operator adds single attributes
* from the original set, creates new ones and deselect single attributes. For
* each freshly added attribute a fourier generation will will be performed.
*
* @see FourierGGA
* @author Ingo Mierswa
* @version $Id: FourierGeneratingMutation.java,v 1.1 2006/04/14 07:47:17
* ingomierswa Exp $
*/
public class FourierGeneratingMutation extends IndividualOperator {
private List<FeatureGenerator> generators;
private List<Attribute> originalAttributes;
private double p;
private int numberOfConstructed;
private int numberOfOriginal;
private SinusFactory factory = null;
private int maxDepth;
private String[] unusableFunctions = new String[0];
private RandomGenerator random;
public FourierGeneratingMutation(List<Attribute> originalAttributes, double p, List<FeatureGenerator> generators, int numberOfConstructed, int numberOfOriginal, int maxPeaks, int adaptionType, int attributesPerPeak, double epsilon, int maxDepth, String[] unusableFunctions, RandomGenerator random) {
this.originalAttributes = originalAttributes;
this.p = p;
this.generators = generators;
this.numberOfConstructed = numberOfConstructed;
this.numberOfOriginal = numberOfOriginal;
this.factory = new SinusFactory(maxPeaks);
this.factory.setAdaptionType(adaptionType);
this.factory.setEpsilon(epsilon);
this.factory.setAttributePerPeak(attributesPerPeak);
this.maxDepth = maxDepth;
this.unusableFunctions = unusableFunctions;
this.random = random;
}
/**
* Performs one of the following three mutations:
* <ul>
* <li>add a newly generated attribute</li>
* <li>add an original attribute</li>
* <li>remove an attribute</li>
* </ul>
*/
public List<Individual> operate(Individual individual) throws Exception {
LinkedList<Individual> l = new LinkedList<Individual>();
AttributeWeightedExampleSet clone = (AttributeWeightedExampleSet) individual.getExampleSet().clone();
try {
int numberOriginal = addOriginalAttribute(clone);
int numberCreated = addGeneratedAttribute(clone);
deselect(clone, numberOriginal + numberCreated);
} catch (GenerationException e) {
individual.getExampleSet().getLog().logWarning("GeneratingMutation: Exception occured during generation of attributes, using only original example set instead.");
}
if (clone.getNumberOfUsedAttributes() > 0) {
l.add(new Individual(clone));
}
l.add(individual);
return l;
}
/** Adds a new attribute. Returns true, if generation was possible. */
private int addGeneratedAttribute(AttributeWeightedExampleSet exampleSet) throws OperatorException {
int counter = 0;
for (int k = 0; k < numberOfConstructed; k++) {
if (random.nextDouble() < p) {
FeatureGenerator generator = FeatureGenerator.selectGenerator(exampleSet, generators, maxDepth, unusableFunctions, random);
if (generator != null) {
generator = generator.newInstance();
Attribute[] args = Tools.getRandomCompatibleAttributes(exampleSet, generator, maxDepth, unusableFunctions, random);
generator.setArguments(args);
List<FeatureGenerator> generatorList = new LinkedList<FeatureGenerator>();
generatorList.add(generator);
List<Attribute> newAttributes = FeatureGenerator.generateAll(exampleSet.getExampleTable(), generatorList);
for (Attribute newAttribute : newAttributes)
exampleSet.getAttributes().addRegular(newAttribute);
counter += newAttributes.size();
Iterator<Attribute> i = newAttributes.iterator();
List<AttributePeak> sinAttributes = new LinkedList<AttributePeak>();
Attribute label = exampleSet.getAttributes().getLabel();
while (i.hasNext()) {
Attribute current = i.next();
if (current.isNumerical() && (current.getConstruction().getDescription().indexOf("sin") == -1)) {
List<AttributePeak> peaks = factory.getAttributePeaks(exampleSet, label, current);
sinAttributes.addAll(peaks);
}
}
if (sinAttributes.size() > 0)
factory.generateSinusFunctions(exampleSet, sinAttributes, random);
counter += sinAttributes.size();
}
}
}
return counter;
}
private int addOriginalAttribute(AttributeWeightedExampleSet exampleSet) throws GenerationException, OperatorException {
int counter = 0;
for (int k = 0; k < numberOfOriginal; k++) {
if (random.nextDouble() < p) {
int i = random.nextInt(originalAttributes.size());
Attribute originalAttribute = originalAttributes.get(i);
if (exampleSet.getAttributes().getRegular(originalAttribute.getName()) == null) {
exampleSet.getAttributes().addRegular(originalAttribute);
counter++;
}
// add sinus functions of the original attribute
List<AttributePeak> peaks = factory.getAttributePeaks(exampleSet, exampleSet.getAttributes().getLabel(), originalAttribute);
if (peaks.size() > 0)
factory.generateSinusFunctions(exampleSet, peaks, random);
counter += peaks.size();
}
}
return counter;
}
private void deselect(AttributeWeightedExampleSet exampleSet, int numberNew) {
double[] probs = Tools.getInverseProbabilitiesFromWeights(exampleSet.getAttributes().createRegularAttributeArray(), exampleSet);
Iterator<Attribute> i = exampleSet.getAttributes().iterator();
int index = 0;
while (i.hasNext()) {
i.next();
if (random.nextDouble() < p * probs[index++] * numberNew) {
i.remove();
}
}
}
}