/*
* 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.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Tools;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
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.InputPort;
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.UndefinedParameterError;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
/**
* <p>
* This operator uses input attribute weights to determine the order of features
* added to the feature set starting with the feature set containing only the
* feature with highest weight. The inner operators must provide a performance
* vector to determine the fitness of the current feature set, e.g. a cross
* validation of a learning scheme for a wrapper evaluation. Stops if adding the
* last <code>k</code> features does not increase the performance or if all
* features were added. The value of <code>k</code> can be set with the
* parameter <code>generations_without_improval</code>.
* </p>
*
* @author Ingo Mierswa
*/
public class WeightGuidedSelectionOperator extends FeatureOperator {
/** The parameter name for "Stop after n generations without improvement of the performance (-1: stops if the number of features is reached)." */
public static final String PARAMETER_GENERATIONS_WITHOUT_IMPROVAL = "generations_without_improval";
/** The parameter name for "Indicates that the absolute values of the input weights should be used to determine the feature adding order." */
public static final String PARAMETER_USE_ABSOLUTE_WEIGHTS = "use_absolute_weights";
public static final String PARAMETER_USE_EARLY_STOPPING = "use_early_stopping";
private int generationsWOImp;
private int maxGenerations;
private int maxWeightIndex = -1;
private InputPort attributeWeightsInput = getInputPorts().createPort("attribute weights in", AttributeWeights.class);
public WeightGuidedSelectionOperator(OperatorDescription description) {
super(description);
}
@Override
protected ExampleSetMetaData modifyInnerOutputExampleSet(ExampleSetMetaData metaData) {
metaData.attributesAreSubset();
return metaData;
}
@Override
protected ExampleSetMetaData modifyOutputExampleSet(ExampleSetMetaData metaData) {
metaData.attributesAreSubset();
return metaData;
}
/**
* Returns an example set containing only the feature with the biggest
* weight.
*/
@Override
public Population createInitialPopulation(ExampleSet es) throws UndefinedParameterError {
this.generationsWOImp = getParameterAsInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL);
this.maxGenerations = es.getAttributes().size();
Population initP = new Population();
double[] weights = new double[es.getAttributes().size()];
if (maxWeightIndex >= 0)
weights[maxWeightIndex] = 1.0d;
else
weights[0] = 1.0d;
initP.add(new Individual(weights));
return initP;
}
/** The operators add the feature with the next highest weight. */
@Override
public List<PopulationOperator> getPreEvaluationPopulationOperators(ExampleSet input) throws OperatorException {
List<PopulationOperator> preOp = new LinkedList<PopulationOperator>();
String[] attributeNames = Tools.getRegularAttributeNames(input);
AttributeWeights attributeWeights = attributeWeightsInput.getData();
attributeWeights.sortByWeight(attributeNames, AttributeWeights.DECREASING, getParameterAsBoolean(PARAMETER_USE_ABSOLUTE_WEIGHTS) ? AttributeWeights.ABSOLUTE_WEIGHTS : AttributeWeights.ORIGINAL_WEIGHTS);
int[] attributeIndices = new int[input.getAttributes().size()];
int counter = 0;
for (String name : attributeNames) {
int index = 0;
for (Attribute attribute : input.getAttributes()) {
if (attribute.getName().equals(name)) {
attributeIndices[counter] = index;
break;
}
index++;
}
counter++;
}
this.maxWeightIndex = attributeIndices[0];
preOp.add(new IterativeFeatureAdding(attributeIndices, 1));
return preOp;
}
/** Returns an empty list. */
@Override
public List<PopulationOperator> getPostEvaluationPopulationOperators(ExampleSet input) throws OperatorException {
return new LinkedList<PopulationOperator>();
}
/**
* Returns true if the best individual is not better than the last
* generation's best individual.
*/
@Override
public boolean solutionGoodEnough(Population pop) throws OperatorException {
return pop.empty() || ((generationsWOImp > 0) && (pop.getGenerationsWithoutImproval() >= generationsWOImp)) || (pop.getGeneration() >= maxGenerations);
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = new LinkedList<ParameterType>();
types.add(new ParameterTypeBoolean(PARAMETER_USE_EARLY_STOPPING, "Enables early stopping. If unchecked, always the maximum number of generations is performed.", false));
ParameterType type = (new ParameterTypeInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL, "Stop criterion: Stop after n generations without improval of the performance.", 1, Integer.MAX_VALUE, 2));
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_USE_EARLY_STOPPING, true, true));
types.add(type);
types.add(new ParameterTypeBoolean(PARAMETER_USE_ABSOLUTE_WEIGHTS, "Indicates that the absolute values of the input weights should be used to determine the feature adding order.", true));
types.addAll(super.getParameterTypes());
return types;
}
}