/*
* 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.features.construction;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.generator.FeatureGenerator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
/**
* In contrast to the class
* {@link com.rapidminer.operator.features.selection.GeneticAlgorithm}, the
* {@link GeneratingGeneticAlgorithm} generates new attributes and thus can
* change the length of an individual. Therfore specialized mutation and
* crossover operators are being applied. Generators are chosen at random from a
* list of generators specified by boolean parameters. <br/>
*
* Since this operator does not contain algorithms to extract features from
* value series, it is restricted to example sets with only single attributes.
* For automatic feature extraction from values series the value series plugin
* for RapidMiner written by Ingo Mierswa should be used. It is available at <a
* href="http://rapid-i.com">http://rapid-i.com</a>
*
* @rapidminer.reference Ritthoff/etal/2001a
* @author Ingo Mierswa, Simon Fischer
* ingomierswa Exp $
*/
public class GeneratingGeneticAlgorithm extends AbstractGeneratingGeneticAlgorithm {
/** The parameter name for "Max number of attributes to generate for an individual in one generation." */
public static final String PARAMETER_MAX_NUMBER_OF_NEW_ATTRIBUTES = "max_number_of_new_attributes";
/** The parameter name for "Max total number of attributes in all generations (-1: no maximum)." */
public static final String PARAMETER_MAX_TOTAL_NUMBER_OF_ATTRIBUTES = "max_total_number_of_attributes";
public static final String PARAMETER_LIMIT_MAX_TOTAL_NUMBER_OF_ATTRIBUTES = "limit_max_total_number_of_attributes";
/** The parameter name for "Probability for an individual to be selected for generation." */
public static final String PARAMETER_P_GENERATE = "p_generate";
/** The parameter name for "Probability for an attribute to be changed (-1: 1 / numberOfAtts)." */
public static final String PARAMETER_P_MUTATION = "p_mutation";
public static final String PARAMETER_USE_HEURISTIC_MUTATION_PROBABILITY = "use_heuristic_mutation_probability";
public GeneratingGeneticAlgorithm(OperatorDescription description) {
super(description);
}
/**
* Returns an operator that performs the mutation. Can be overridden by
* subclasses.
*/
@Override
protected ExampleSetBasedPopulationOperator getMutationPopulationOperator(ExampleSet eSet) throws UndefinedParameterError {
double pMutation = getParameterAsBoolean(PARAMETER_USE_HEURISTIC_MUTATION_PROBABILITY) ? -1 : getParameterAsDouble(PARAMETER_P_MUTATION);
return new ExampleSetBasedSelectionMutation(pMutation, getRandom(), 1, getParameterAsInt(PARAMETER_MAX_TOTAL_NUMBER_OF_ATTRIBUTES), -1);
}
/** Returns a specialized mutation, i.e. a <code>AttributeGenerator</code> */
@Override
protected ExampleSetBasedPopulationOperator getGeneratingPopulationOperator(ExampleSet eSet) throws UndefinedParameterError {
List<FeatureGenerator> generators = getGenerators();
if (generators.size() == 0) {
logWarning("No FeatureGenerators specified for " + getName() + ".");
}
int noOfNewAttributes = getParameterAsInt(PARAMETER_MAX_NUMBER_OF_NEW_ATTRIBUTES);
int totalNoOfNewAttributes = getParameterAsBoolean(PARAMETER_LIMIT_MAX_TOTAL_NUMBER_OF_ATTRIBUTES) ? -1 : getParameterAsInt(PARAMETER_MAX_TOTAL_NUMBER_OF_ATTRIBUTES);
double pGenerate = getParameterAsDouble(PARAMETER_P_GENERATE);
return new AttributeGenerator(pGenerate, noOfNewAttributes, totalNoOfNewAttributes, generators, getRandom());
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = new LinkedList<ParameterType>();
ParameterType type = new ParameterTypeInt(PARAMETER_MAX_NUMBER_OF_NEW_ATTRIBUTES, "Max number of attributes to generate for an individual in one generation.", 0, Integer.MAX_VALUE, 1);
type.setExpert(false);
types.add(type);
type = new ParameterTypeBoolean(PARAMETER_LIMIT_MAX_TOTAL_NUMBER_OF_ATTRIBUTES, "Indicates if the total number of attributes in all generations should be limited.", false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_MAX_TOTAL_NUMBER_OF_ATTRIBUTES, "Max total number of attributes in all generations.", 1, Integer.MAX_VALUE, 1);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_LIMIT_MAX_TOTAL_NUMBER_OF_ATTRIBUTES, false, true));
types.add(type);
types.addAll(super.getParameterTypes());
type = new ParameterTypeDouble(PARAMETER_P_GENERATE, "Probability for an individual to be selected for generation.", 0, 1, 0.1);
types.add(type);
types.add(new ParameterTypeBoolean(PARAMETER_USE_HEURISTIC_MUTATION_PROBABILITY, "If checked the probability for mutations will be chosen as 1/number of attributes.", true));
type = new ParameterTypeDouble(PARAMETER_P_MUTATION, "Probability for mutation.", 0, 1);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_USE_HEURISTIC_MUTATION_PROBABILITY, false, false));
types.add(type);
return types;
}
}