/*
* 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.selection;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.AttributeWeightedExampleSet;
import com.rapidminer.operator.IOObject;
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.KeepBest;
import com.rapidminer.operator.features.Population;
import com.rapidminer.operator.features.PopulationOperator;
import com.rapidminer.operator.features.RedundanceRemoval;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
/**
* <p>
* This operator realizes the two deterministic greedy feature selection
* algorithms forward selection and backward elimination. However, we added some
* enhancements to the standard algorithms which are described below:
* </p>
*
* <h4>Forward Selection</h4>
* <ol>
* <li>Create an initial population with {@rapidminer.math n} individuals where
* {@rapidminer.math n} is the input example set's number of attributes. Each
* individual will use exactly one of the features.</li>
* <li>Evaluate the attribute sets and select only the best {@rapidminer.math k}.</li>
* <li>For each of the {@rapidminer.math k} attribute sets do: If there are
* {@rapidminer.math j} unused attributes, make {@rapidminer.math j} copies of the attribute
* set and add exactly one of the previously unused attributes to the attribute
* set.</li>
* <li>As long as the performance improved in the last {@rapidminer.math p}
* iterations go to 2</li>
* </ol>
*
* <h4>Backward Elimination</h4>
* <ol>
* <li>Start with an attribute set which uses all features.</li>
* <li>Evaluate all attribute sets and select the best {@rapidminer.math k}.</li>
* <li>For each of the {@rapidminer.math k} attribute sets do: If there are
* {@rapidminer.math j} attributes used, make {@rapidminer.math j} copies of the attribute
* set and remove exactly one of the previously used attributes from the
* attribute set.</li>
* <li>As long as the performance improved in the last {@rapidminer.math p}
* iterations go to 2</li>
* </ol>
*
* <p>
* The parameter {@rapidminer.math k} can be specified by the parameter
* <code>keep_best</code>, the parameter {@rapidminer.math p} can be specified by
* the parameter <code>generations_without_improval</code>. These parameters
* have default values 1 which means that the standard selection algorithms are
* used. Using other values increase the runtime but might help to avoid local
* extrema in the search for the global optimum.
* </p>
*
* <p>
* Another unusual parameter is <code>maximum_number_of_generations</code>.
* This parameter bounds the number of iterations to this maximum of feature
* selections / deselections. In combination with
* <code>generations_without_improval</code> this allows several different
* selection schemes (which are described for forward selection, backward
* elimination works analogous):
*
* <ul>
* <li><code>maximum_number_of_generations</code> = {@rapidminer.math m} and
* <code>generations_without_improval</code> = {@rapidminer.math p}: Selects
* maximal {@rapidminer.math m} features. The selection stops if not performance
* improvement was measured in the last {@rapidminer.math p} generations.</li>
* <li><code>maximum_number_of_generations</code> = {@rapidminer.math -1} and
* <code>generations_without_improval</code> = {@rapidminer.math p}: Tries to
* selects new features until no performance improvement was measured in the
* last {@rapidminer.math p} generations.</li>
* <li><code>maximum_number_of_generations</code> = {@rapidminer.math m} and
* <code>generations_without_improval</code> = {@rapidminer.math -1}: Selects
* maximal {@rapidminer.math m} features. The selection stops is not stopped until all
* combinations with maximal {@rapidminer.math m} were tried. However, the result
* might contain less features than these.</li>
* <li><code>maximum_number_of_generations</code> = {@rapidminer.math -1} and
* <code>generations_without_improval</code> = {@rapidminer.math -1}: Test all
* combinations of attributes (brute force, this might take a very long time and
* should only be applied to small attribute sets).</li>
* </ul>
* </p>
*
* @author Simon Fischer, Ingo Mierswa
* @version $Id: FeatureSelectionOperator.java,v 1.1 2006/04/14 11:42:27
* ingomierswa Exp $
*/
public class FeatureSelectionOperator extends FeatureOperator {
/** The parameter name for "Forward selection or backward elimination." */
public static final String PARAMETER_SELECTION_DIRECTION = "selection_direction";
/** The parameter name for "Keep the best n individuals in each generation." */
public static final String PARAMETER_KEEP_BEST = "keep_best";
/** The parameter name for "Stop after n generations without improval of the performance (-1: stops if the maximum_number_of_generations is reached)." */
public static final String PARAMETER_GENERATIONS_WITHOUT_IMPROVAL = "generations_without_improval";
/** The parameter name for "Delivers the maximum amount of generations (-1: might use or deselect all features)." */
public static final String PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS = "maximum_number_of_generations";
public static final int FORWARD_SELECTION = 0;
public static final int BACKWARD_ELIMINATION = 1;
private static final String[] DIRECTIONS = { "forward", "backward" };
private int generationsWOImp;
private int maxGenerations;
public FeatureSelectionOperator(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
this.maxGenerations = getParameterAsInt(PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS);
this.generationsWOImp = getParameterAsInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL);
return super.apply();
}
int getDefaultDirection() {
return FORWARD_SELECTION;
}
/**
* May <tt>es</tt> have <i>n</i> features. The initial population
* contains (depending on wether forward selection or backward elimination
* is used) either
* <ul>
* <li><i>n</i> elements with exactly 1 feature switched on or
* <li>1 element with all <i>n</i> features switched on.
* </ul>
*/
public Population createInitialPopulation(ExampleSet es) throws UndefinedParameterError {
int direction = getParameterAsInt(PARAMETER_SELECTION_DIRECTION);
Population initP = new Population();
if (direction == FORWARD_SELECTION) {
AttributeWeightedExampleSet nes = new AttributeWeightedExampleSet((ExampleSet) es.clone());
nes.getAttributes().clearRegular();
for (Attribute attribute : es.getAttributes()) {
AttributeWeightedExampleSet forwardES = (AttributeWeightedExampleSet) nes.clone();
forwardES.getAttributes().addRegular(attribute);
if (forwardES.getNumberOfUsedAttributes() > 0)
initP.add(new Individual(forwardES));
}
} else {
AttributeWeightedExampleSet nes = new AttributeWeightedExampleSet((ExampleSet) es.clone());
for (Attribute attribute : nes.getAttributes()) {
nes.setAttributeUsed(attribute, true);
}
if (nes.getNumberOfUsedAttributes() > 0)
initP.add(new Individual(nes));
}
return initP;
}
/**
* The operators performs two steps:
* <ol>
* <li>forward selection/backward elimination
* <li>kick out all but the <tt>keep_best</tt> individuals
* <li>remove redundant individuals
* </ol>
*/
public List<PopulationOperator> getPreEvaluationPopulationOperators(ExampleSet input) throws OperatorException {
int direction = getParameterAsInt(PARAMETER_SELECTION_DIRECTION);
int keepBest = getParameterAsInt(PARAMETER_KEEP_BEST);
List<PopulationOperator> preOp = new LinkedList<PopulationOperator>();
preOp.add(new KeepBest(keepBest));
if (direction == FORWARD_SELECTION) {
preOp.add(new ForwardSelection(input));
if (this.maxGenerations <= 0)
this.maxGenerations = input.getAttributes().size() - 1;
else
this.maxGenerations--; // ensures the correct number of
// features
} else {
preOp.add(new BackwardElimination());
if (this.maxGenerations <= 0)
this.maxGenerations = input.getAttributes().size();
}
preOp.add(new RedundanceRemoval());
return preOp;
}
/** empty list */
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.
*/
public boolean solutionGoodEnough(Population pop) throws OperatorException {
return pop.empty() || ((generationsWOImp > 0) && (pop.getGenerationsWithoutImproval() >= generationsWOImp)) || (pop.getGeneration() >= maxGenerations);
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeCategory(PARAMETER_SELECTION_DIRECTION, "Forward selection or backward elimination.", DIRECTIONS, getDefaultDirection());
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeInt(PARAMETER_KEEP_BEST, "Keep the best n individuals in each generation.", 1, Integer.MAX_VALUE, 1));
types.add(new ParameterTypeInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL, "Stop after n generations without improval of the performance (-1: stops if the maximum_number_of_generations is reached).", -1, Integer.MAX_VALUE, 1));
types.add(new ParameterTypeInt(PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS, "Delivers the maximum amount of generations (-1: might use or deselect all features).", -1, Integer.MAX_VALUE, -1));
return types;
}
}