/*
* 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.aggregation;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Random;
import com.rapidminer.datatable.SimpleDataTable;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.generator.AlgebraicOrGenerator;
import com.rapidminer.generator.FeatureGenerator;
import com.rapidminer.generator.MinMaxGenerator;
import com.rapidminer.operator.OperatorChain;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetPassThroughRule;
import com.rapidminer.operator.ports.metadata.SetRelation;
import com.rapidminer.operator.ports.metadata.SubprocessTransformRule;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeFile;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.conditions.EqualTypeCondition;
import com.rapidminer.tools.RandomGenerator;
/**
* Performs an evolutionary feature aggregation. Each base feature is only
* allowed to be used as base feature, in one merged feature, or it may not be
* used at all.
*
* @author Ingo Mierswa
*/
public class EvolutionaryFeatureAggregation extends OperatorChain {
public static final String PARAMETER_POPULATION_CRITERIA_DATA_FILE = "population_criteria_data_file";
public static final String PARAMETER_AGGREGATION_FUNCTION = "aggregation_function";
public static final String PARAMETER_POPULATION_SIZE = "population_size";
public static final String PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS = "maximum_number_of_generations";
public static final String PARAMETER_SELECTION_TYPE = "selection_type";
public static final String PARAMETER_TOURNAMENT_FRACTION = "tournament_fraction";
public static final String PARAMETER_CROSSOVER_TYPE = "crossover_type";
public static final String PARAMETER_P_CROSSOVER = "p_crossover";
/** The names for the selection types. */
private static final String[] SELECTION_TYPES = { "tournament", "non-dominated" };
/** Indicates tournament selection. */
private static final int SELECTION_TOURNAMENT = 0;
/** Indicates NSGA-II selection. */
private static final int SELECTION_MO = 1;
/** The names for the aggregation functions. */
private static final String[] AGGREGATION_FUNCTIONS = { "maximum", "algebraic_or" };
/** Indicates the maximum aggregation function. */
private static final int AGGREGATION_MAX = 0;
/** Indicates the algebraic OR aggregation function. */
private static final int AGGREGATION_ALGEBRAIC = 1;
/** The original attributes. */
private Attribute[] allAttributes;
/** The used feature generator. */
private FeatureGenerator generator = new MinMaxGenerator(MinMaxGenerator.MAX);
/** The current generation. */
private int generation = 0;
/** The maximum generation. */
private int maxGeneration = 100;
private final InputPort exampleSetInput = getInputPorts().createPort("example set in", ExampleSet.class);
private final OutputPort innerExampleSetSource = getSubprocess(0).getInnerSources().createPort("example set source") ;
private final InputPort innerPerformanceSink = getSubprocess(0).getInnerSinks().createPort("performance vector sink", PerformanceVector.class);
private final OutputPort exampleSetOutput = getOutputPorts().createPort("example set out");
private final OutputPort performanceOutput = getOutputPorts().createPort("performance vector out");
/** Creates a new evolutionary feature aggregation algorithm. */
public EvolutionaryFeatureAggregation(OperatorDescription description) {
super(description, "Performance Evaluation");
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, exampleSetOutput, SetRelation.SUBSET) {
@Override
public ExampleSetMetaData modifyExampleSet(ExampleSetMetaData metaData) {
for (AttributeMetaData amd: metaData.getAllAttributes()) {
if (amd.isNumerical()) {
amd.setValueSetRelation(SetRelation.UNKNOWN);
}
}
return metaData;
}
});
getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, innerExampleSetSource, SetRelation.SUBSET) {
@Override
public ExampleSetMetaData modifyExampleSet(ExampleSetMetaData metaData) {
for (AttributeMetaData amd: metaData.getAllAttributes()) {
if (amd.isNumerical()) {
amd.setValueSetRelation(SetRelation.UNKNOWN);
}
}
return metaData;
}
});
getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
getTransformer().addPassThroughRule(innerPerformanceSink, performanceOutput);
}
@Override
public void doWork() throws OperatorException {
// init
ExampleSet exampleSet = exampleSetInput.getData();
int popSize = getParameterAsInt(PARAMETER_POPULATION_SIZE);
this.generation = 0;
this.maxGeneration = getParameterAsInt(PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS);
int functionType = getParameterAsInt(PARAMETER_AGGREGATION_FUNCTION);
switch (functionType) {
case AGGREGATION_MAX:
this.generator = new MinMaxGenerator(MinMaxGenerator.MAX);
break;
case AGGREGATION_ALGEBRAIC:
this.generator = new AlgebraicOrGenerator();
break;
}
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
this.allAttributes = new Attribute[exampleSet.getAttributes().size()];
int index = 0;
for (Attribute attribute : exampleSet.getAttributes())
allAttributes[index++] = attribute;
// plotter
AggregationPopulationPlotter plotter = new AggregationPopulationPlotter(exampleSet, allAttributes, this.generator);
// crossover
AggregationCrossover crossover = new AggregationCrossover(getParameterAsInt(PARAMETER_CROSSOVER_TYPE), getParameterAsDouble(PARAMETER_P_CROSSOVER), random);
// mutation
AggregationMutation mutation = new AggregationMutation(random);
// selection
int selectionType = getParameterAsInt(PARAMETER_SELECTION_TYPE);
AggregationSelection selection = null;
switch (selectionType) {
case SELECTION_TOURNAMENT:
selection = new AggregationTournamentSelection(popSize, getParameterAsDouble(PARAMETER_TOURNAMENT_FRACTION), random);
break;
case SELECTION_MO:
selection = new AggregationNonDominatedSortingSelection(popSize);
break;
}
// initial population
List<AggregationIndividual> population = createInitialPopulation(popSize, exampleSet.getAttributes().size(), random);
// start optimization loop
while (!solutionGoodEnough()) {
generation++;
crossover.crossover(population);
mutation.mutate(population);
evaluate(population, exampleSet);
selection.performSelection(population);
plotter.operate(population);
inApplyLoop();
}
// write criteria data of the final population into a file
if (isParameterSet(PARAMETER_POPULATION_CRITERIA_DATA_FILE)) {
File outFile = getParameterAsFile(PARAMETER_POPULATION_CRITERIA_DATA_FILE, true);
SimpleDataTable finalStatistics = plotter.createDataTable(population);
plotter.fillDataTable(finalStatistics, population);
PrintWriter out = null;
try {
out = new PrintWriter(new FileWriter(outFile));
finalStatistics.write(out);
} catch (IOException e) {
throw new UserError(this, e, 303, new Object[] { outFile, e.getMessage() });
} finally {
if (out != null) {
out.close();
}
}
}
// return result
evaluate(population, exampleSet);
Iterator<AggregationIndividual> i = population.iterator();
AggregationIndividual bestEver = null;
PerformanceVector bestPerformance = null;
while (i.hasNext()) {
AggregationIndividual current = i.next();
PerformanceVector currentPerf = current.getPerformance();
if ((bestPerformance == null) || (currentPerf.compareTo(bestPerformance) > 0)) {
bestPerformance = currentPerf;
bestEver = current;
}
}
exampleSetOutput.deliver(bestEver.createExampleSet(exampleSet, allAttributes, generator));
performanceOutput.deliver(bestPerformance);
}
// ================================================================================
private List<AggregationIndividual> createInitialPopulation(int popSize, int individualSize, Random random) {
List<AggregationIndividual> population = new ArrayList<AggregationIndividual>();
for (int i = 0; i < popSize; i++) {
int[] individual = new int[individualSize];
for (int a = 0; a < individual.length; a++) {
if (random.nextBoolean()) {
individual[a] = 0;
} else {
individual[a] = 1;
}
}
population.add(new AggregationIndividual(individual));
}
return population;
}
/** Returns true if the maximum number of generations was reached. */
private boolean solutionGoodEnough() {
if (generation > maxGeneration)
return true;
else
return false;
}
/**
* Creates example sets from all individuals and invoke the inner operators
* in order to estimate the performance.
*/
public void evaluate(List population, ExampleSet originalExampleSet) throws OperatorException {
Iterator i = population.iterator();
while (i.hasNext()) {
AggregationIndividual individual = (AggregationIndividual) i.next();
if (individual.getPerformance() == null) {
ExampleSet exampleSet = individual.createExampleSet(originalExampleSet, allAttributes, generator);
if (exampleSet.getAttributes().size() == 0) {
i.remove();
} else {
innerExampleSetSource.deliver(exampleSet);
getSubprocess(0).execute();
PerformanceVector performanceVector = innerPerformanceSink.getData();
individual.setPerformance(performanceVector);
}
}
}
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeCategory(PARAMETER_AGGREGATION_FUNCTION, "The aggregation function which is used for feature aggregations.", AGGREGATION_FUNCTIONS, AGGREGATION_MAX);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_POPULATION_SIZE, "Number of individuals per generation.", 1, Integer.MAX_VALUE, 10);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS, "Number of generations after which to terminate the algorithm.", 1, Integer.MAX_VALUE, 100);
type.setExpert(false);
types.add(type);
type = new ParameterTypeCategory(PARAMETER_SELECTION_TYPE, "The type of selection.", SELECTION_TYPES, SELECTION_TOURNAMENT);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_TOURNAMENT_FRACTION, "The fraction of the population which will participate in each tournament.", 0.0d, 1.0d, 0.2d);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_SELECTION_TYPE, SELECTION_TYPES, false, SELECTION_TOURNAMENT));
types.add(type);
types.add(new ParameterTypeCategory(PARAMETER_CROSSOVER_TYPE, "The type of crossover.", AggregationCrossover.CROSSOVER_TYPES, AggregationCrossover.CROSSOVER_UNIFORM));
types.add(new ParameterTypeDouble(PARAMETER_P_CROSSOVER, "Probability for an individual to be selected for crossover.", 0.0d, 1.0d, 0.9d));
types.add(new ParameterTypeFile(PARAMETER_POPULATION_CRITERIA_DATA_FILE, "The path to the file in which the criteria data of the final population should be saved.", "crit", true));
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
}