/*
* 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.selection;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.features.FeatureOperator;
import com.rapidminer.operator.features.Individual;
import com.rapidminer.operator.features.Population;
import com.rapidminer.operator.features.PopulationOperator;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
/**
* This feature selection operator selects the best attribute set by trying all
* possible combinations of attribute selections. It returns the example set
* containing the subset of attributes which produced the best performance. As
* this operator works on the powerset of the attributes set it has exponential
* runtime.
*
* @author Simon Fischer, Ingo Mierswa
*/
public class BruteForceSelection extends FeatureOperator {
public static final String PARAMETER_USE_EXACT_NUMBER = "use_exact_number_of_attributes";
public static final String PARAMETER_RESTRICT_NUMBER = "restrict_maximum";
public static final String PARAMETER_MIN_NUMBER_OF_ATTRIBUTES = "min_number_of_attributes";
public static final String PARAMETER_MAX_NUMBER_OF_ATTRIBUTES = "max_number_of_attributes";
public static final String PARAMETER_EXACT_NUMBER_OF_ATTRIBUTES = "exact_number_of_attributes";
public BruteForceSelection(OperatorDescription description) {
super(description);
}
@Override
protected ExampleSetMetaData modifyInnerOutputExampleSet(ExampleSetMetaData metaData) {
metaData.attributesAreSubset();
return metaData;
}
@Override
protected ExampleSetMetaData modifyOutputExampleSet(ExampleSetMetaData metaData) {
metaData.attributesAreSubset();
return metaData;
}
@Override
public Population createInitialPopulation(ExampleSet es) throws OperatorException {
int minNumberOfFeatures = getParameterAsInt(PARAMETER_MIN_NUMBER_OF_ATTRIBUTES);
int maxNumberOfFeatures = getParameterAsInt(PARAMETER_MAX_NUMBER_OF_ATTRIBUTES);
int exactNumberOfFeatures = getParameterAsInt(PARAMETER_EXACT_NUMBER_OF_ATTRIBUTES);
if (getParameterAsBoolean(PARAMETER_USE_EXACT_NUMBER)) {
logNote("Using exact number of features for feature selection (" + exactNumberOfFeatures + "), ignoring possibly defined range for the number of features.");
} else {
if (!getParameterAsBoolean(PARAMETER_RESTRICT_NUMBER)) {
maxNumberOfFeatures = es.getAttributes().size();
} else {
if (minNumberOfFeatures > maxNumberOfFeatures)
throw new UserError(this, 210, PARAMETER_MAX_NUMBER_OF_ATTRIBUTES, PARAMETER_MIN_NUMBER_OF_ATTRIBUTES);
}
}
Population pop = new Population();
double[] weights = new double[es.getAttributes().size()];
if (getParameterAsBoolean(PARAMETER_USE_EXACT_NUMBER)) {
addAllWithExactNumber(pop, weights, 0, exactNumberOfFeatures);
} else {
addAllInRange(pop, weights, 0, minNumberOfFeatures, maxNumberOfFeatures);
}
return pop;
}
/** Add all attribute combinations with a fixed size to the population. */
private void addAllWithExactNumber(Population pop, double[] weights, int startIndex, int exactNumberOfFeatures) {
Individual individual = new Individual(weights);
if (individual.getNumberOfUsedAttributes() > exactNumberOfFeatures)
return;
for (int i = startIndex; i < weights.length; i++) {
double[] clone = individual.getWeightsClone();
clone[i] = 1.0d;
Individual newIndividual = new Individual(clone);
if (newIndividual.getNumberOfUsedAttributes() == exactNumberOfFeatures) {
pop.add(newIndividual);
} else {
addAllWithExactNumber(pop, clone, i + 1, exactNumberOfFeatures);
}
}
}
/** Recursive method to add all attribute combinations to the population. */
private void addAllInRange(Population pop, double[] weights, int startIndex,
int minNumberOfFeatures, int maxNumberOfFeatures) {
if (startIndex >= weights.length)
return;
Individual individual = new Individual(weights);
int numberOfFeatures = individual.getNumberOfUsedAttributes();
if (maxNumberOfFeatures > 0) {
if (numberOfFeatures > maxNumberOfFeatures) {
return;
}
}
// recursive call
double[] clone = individual.getWeightsClone();
clone[startIndex] = 0;
addAllInRange(pop, clone, startIndex + 1, minNumberOfFeatures, maxNumberOfFeatures);
double[] clone2 = individual.getWeightsClone();
clone2[startIndex] = 1.0d;
Individual newIndividual = new Individual(clone2);
numberOfFeatures = newIndividual.getNumberOfUsedAttributes();
if (numberOfFeatures > 0) {
if (((maxNumberOfFeatures < 1) || (numberOfFeatures <= maxNumberOfFeatures)) && (numberOfFeatures >= minNumberOfFeatures)) {
pop.add(newIndividual);
}
}
addAllInRange(pop, clone2, startIndex + 1, minNumberOfFeatures, maxNumberOfFeatures);
}
/** Does nothing. */
@Override
public List<PopulationOperator> getPreEvaluationPopulationOperators(ExampleSet input) throws OperatorException {
return new LinkedList<PopulationOperator>();
}
/** Returns an empty list if the parameter debug_output is set to false. */
@Override
public List<PopulationOperator> getPostEvaluationPopulationOperators(ExampleSet input) throws OperatorException {
return new LinkedList<PopulationOperator>();
}
/** Stops immediately. */
@Override
public boolean solutionGoodEnough(Population pop) {
return true;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = new LinkedList<ParameterType>();
ParameterType type = new ParameterTypeBoolean(PARAMETER_USE_EXACT_NUMBER, "Determines if only combinations containing this numbers of attributes should be tested.", false);
type.setExpert(false);
types.add(type);
type = new ParameterTypeBoolean(PARAMETER_RESTRICT_NUMBER, "If checked the maximal number of attributes might be restricted. Otherwise all combinations of all number of attributes are generated and tested.", false);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_USE_EXACT_NUMBER, false, false));
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_MIN_NUMBER_OF_ATTRIBUTES, "Determines the minimum number of features used for the combinations.", 1, Integer.MAX_VALUE, 1);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_USE_EXACT_NUMBER, true, false));
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_MAX_NUMBER_OF_ATTRIBUTES, "Determines the maximum number of features used for the combinations.", 1, Integer.MAX_VALUE, 1);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_RESTRICT_NUMBER, true, true));
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_USE_EXACT_NUMBER, true, false));
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_EXACT_NUMBER_OF_ATTRIBUTES, "Determines the exact number of features used for the combinations.", 1, Integer.MAX_VALUE, 1);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_USE_EXACT_NUMBER, true, true));
type.setExpert(false);
types.add(type);
types.addAll(super.getParameterTypes());
return types;
}
}