/*
* 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.learner.functions.kernel.evosvm;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.datatable.DataTable;
import com.rapidminer.datatable.SimpleDataTable;
import com.rapidminer.datatable.SimpleDataTableRow;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.gui.plotter.ScatterPlotter;
import com.rapidminer.gui.plotter.SimplePlotterDialog;
import com.rapidminer.operator.IOContainer;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCreationException;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.functions.kernel.SupportVector;
import com.rapidminer.operator.learner.functions.kernel.functions.Kernel;
import com.rapidminer.operator.performance.EstimatedPerformance;
import com.rapidminer.operator.performance.PerformanceEvaluator;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.tools.LoggingHandler;
import com.rapidminer.tools.OperatorService;
import com.rapidminer.tools.RandomGenerator;
import com.rapidminer.tools.math.optimization.ec.es.ESOptimization;
import com.rapidminer.tools.math.optimization.ec.es.Individual;
import com.rapidminer.tools.math.optimization.ec.es.NonDominatedSortingSelection;
import com.rapidminer.tools.math.optimization.ec.es.Population;
/**
* Evolutionary Strategy approach for SVM optimization. This class can be used for classification
* problems.
*
* @author Ingo Mierswa
* @version $Id: ClassificationEvoOptimization.java,v 1.12 2008/07/31 17:07:14 ingomierswa Exp $
*/
public class ClassificationEvoOptimization extends ESOptimization implements EvoOptimization {
/** Number smaller than this number are regarded as zero. */
private final static double IS_ZERO = 1e-8;
/** The training example set. */
private ExampleSet exampleSet;
/** The used kernel function. */
private Kernel kernel;
/** The parameter C indicating the weight of errors. */
private double c;
/** The label values. */
private double[] ys;
/** This function is to maximize. */
private OptimizationFunction optimizationFunction;
/** The hold-out set which can be used to display the generalization performance for the final population. */
private ExampleSet holdOutSet = null;
/** Final population size for plotting. */
private int populationSize = 10;
/** Creates a new evolutionary SVM optimization. */
public ClassificationEvoOptimization(ExampleSet exampleSet, // training data
Kernel kernel, double c, // SVM paras
int initType, // start population creation type para
int maxIterations, int generationsWithoutImprovement, int popSize, // GA paras
int selectionType, double tournamentFraction, boolean keepBest, // selection paras
int mutationType, // type of mutation
double crossoverProb,
boolean showConvergencePlot,
ExampleSet holdOutSet,
RandomGenerator random,
LoggingHandler logging) {
super(EvoSVM.createBoundArray(0.0d, exampleSet.size()),
EvoSVM.determineMax(c, kernel, exampleSet, selectionType, exampleSet.size()), popSize,
exampleSet.size(), initType, maxIterations, generationsWithoutImprovement,
selectionType, tournamentFraction, keepBest,
mutationType, Double.NaN, crossoverProb, showConvergencePlot, random, logging);
this.exampleSet = exampleSet;
this.holdOutSet = holdOutSet;
this.populationSize = popSize;
this.kernel = kernel;
this.c = getMax(0);
// label values
this.ys = new double[exampleSet.size()];
Iterator<Example> reader = exampleSet.iterator();
int index = 0;
Attribute label = exampleSet.getAttributes().getLabel();
while (reader.hasNext()) {
Example example = reader.next();
ys[index++] = example.getLabel() == label.getMapping().getPositiveIndex() ? 1.0d : -1.0d;
}
// optimization function
this.optimizationFunction = new ClassificationOptimizationFunction(selectionType == NON_DOMINATED_SORTING_SELECTION);
}
public PerformanceVector evaluateIndividual(Individual individual) {
double[] fitness = optimizationFunction.getFitness(individual.getValues(), ys, kernel);
PerformanceVector performanceVector = new PerformanceVector();
if (fitness.length == 1) {
performanceVector.addCriterion(new EstimatedPerformance("SVM_fitness", fitness[0], 1, false));
} else {
performanceVector.addCriterion(new EstimatedPerformance("alpha_sum", fitness[0], 1, false));
performanceVector.addCriterion(new EstimatedPerformance("svm_objective_function", fitness[1], 1, false));
if (fitness.length == 3)
performanceVector.addCriterion(new EstimatedPerformance("alpha_label_sum", fitness[2], 1, false));
}
return performanceVector;
}
// ================================================================================
// T R A I N
// ================================================================================
/**
* Trains the SVM. In this case an evolutionary strategy approach is applied
* to determine the best alpha values.
*/
public EvoSVMModel train() throws OperatorException {
optimize();
if (holdOutSet != null) {
//List<Double> maxAlphas = new LinkedList<Double>();
DataTable holdOutSetPerfomance = new SimpleDataTable("Generalization Performance", new String[] { "individual", "training error", "test error" });
Population population = getPopulation();
NonDominatedSortingSelection selection = new NonDominatedSortingSelection(this.populationSize);
selection.operate(population);
//population.sort();
class TrainingTestError implements Comparable<TrainingTestError> {
private double trainingError;
private double testError;
private double[] alphas;
TrainingTestError(double trainingError, double testError, double[] alphas) {
this.trainingError = trainingError;
this.testError = testError;
this.alphas = alphas;
}
public int compareTo(TrainingTestError o) {
return -1 * Double.compare(this.trainingError, o.trainingError);
}
public boolean equals(Object o) {
if (!(o instanceof TrainingTestError))
return false;
return this.trainingError == ((TrainingTestError)o).trainingError;
}
public int hashCode() {
return Double.valueOf(this.trainingError).hashCode();
}
}
List<TrainingTestError> errorList = new LinkedList<TrainingTestError>();
for (int i = 0; i < population.getNumberOfIndividuals(); i++) {
double[] currentValues = population.get(i).getValues();
// calc max alpha (corresponds to C)
/*
double max = Double.NEGATIVE_INFINITY;
for (double alpha : currentValues)
if (alpha > max)
max = alpha;
maxAlphas.add(max);
*/
// calc generalization error on hold-out set
Model model = null;
try {
model = getModel(currentValues);
} catch (IllegalArgumentException e) {
// skip model
}
if (model != null) {
double trainingError = getError(exampleSet, model);
double testError = getError(holdOutSet, model);
errorList.add(new TrainingTestError(trainingError, testError, currentValues));
}
}
Collections.sort(errorList);
Iterator<TrainingTestError> i = errorList.iterator();
int counter = 0;
int bestIndex = -1;
double bestValue = Double.POSITIVE_INFINITY;
while (i.hasNext()) {
TrainingTestError error = i.next();
holdOutSetPerfomance.add(new SimpleDataTableRow(new double[] { counter, error.trainingError, error.testError }));
if (error.testError < bestValue) {
bestIndex = counter;
bestValue = error.testError;
}
counter++;
}
// create plotter
SimplePlotterDialog plotter = new SimplePlotterDialog(holdOutSetPerfomance, false);
plotter.setXAxis(0);
plotter.plotColumn(1, true);
plotter.plotColumn(2, true);
plotter.setPointType(ScatterPlotter.POINTS);
plotter.setVisible(true);
return getModel(errorList.get(bestIndex).alphas);
} else {
return getModel(getBestValuesEver());
}
}
private double getError(ExampleSet exampleSet, Model model) throws OperatorException {
exampleSet = model.apply(exampleSet);
try {
PerformanceEvaluator evaluator = OperatorService.createOperator(PerformanceEvaluator.class);
evaluator.setParameter("classification_error", "true");
IOContainer result = evaluator.apply(new IOContainer(new IOObject[] { exampleSet }));
PerformanceVector performance = result.get(PerformanceVector.class);
return performance.getMainCriterion().getAverage();
} catch (OperatorCreationException e) {
e.printStackTrace();
return Double.NaN;
}
}
/** Delivers the fitness of the best individual as performance vector. */
public PerformanceVector getOptimizationPerformance() {
double[] bestValuesEver = getBestValuesEver();
double[] finalFitness = optimizationFunction.getFitness(bestValuesEver, ys, kernel);
PerformanceVector result = new PerformanceVector();
if (finalFitness.length == 1) {
result.addCriterion(new EstimatedPerformance("svm_objective_function", finalFitness[0], 1, false));
} else {
result.addCriterion(new EstimatedPerformance("alpha_sum", finalFitness[0], 1, false));
result.addCriterion(new EstimatedPerformance("svm_objective_function", finalFitness[1], 1, false));
if (finalFitness.length == 3)
result.addCriterion(new EstimatedPerformance("alpha_label_sum", finalFitness[2], 1, false));
}
return result;
}
// ================================================================================
// C R E A T E M O D E L
// ================================================================================
/**
* Returns a model containing all support vectors, i.e. the examples with
* non-zero alphas.
*/
private EvoSVMModel getModel(double[] alphas) {
// calculate support vectors
Iterator<Example> reader = exampleSet.iterator();
List<SupportVector> supportVectors = new ArrayList<SupportVector>();
int index = 0;
while (reader.hasNext()) {
double currentAlpha = alphas[index];
Example currentExample = reader.next();
if (currentAlpha != 0.0d) {
double[] x = new double[exampleSet.getAttributes().size()];
int a = 0;
for (Attribute attribute : exampleSet.getAttributes())
x[a++] = currentExample.getValue(attribute);
supportVectors.add(new SupportVector(x, ys[index], currentAlpha));
}
index++;
}
// calculate all sum values
double[] sum = new double[exampleSet.size()];
reader = exampleSet.iterator();
index = 0;
while (reader.hasNext()) {
Example current = reader.next();
double[] x = new double[exampleSet.getAttributes().size()];
int a = 0;
for (Attribute attribute : exampleSet.getAttributes())
x[a++] = current.getValue(attribute);
sum[index] = kernel.getSum(supportVectors, x);
index++;
}
// calculate b (from Stefan's mySVM code)
double bSum = 0.0d;
int bCounter = 0;
for (int i = 0; i < alphas.length; i++) {
if ((ys[i] * alphas[i] - c < -IS_ZERO) && (ys[i] * alphas[i] > IS_ZERO)) {
bSum += ys[i] - sum[i];
bCounter++;
} else if ((ys[i] * alphas[i] + c > IS_ZERO) && (ys[i] * alphas[i] < -IS_ZERO)) {
bSum += ys[i] - sum[i];
bCounter++;
}
}
if (bCounter == 0) {
// unlikely
bSum = 0.0d;
for (int i = 0; i < alphas.length; i++) {
if ((ys[i] * alphas[i] < IS_ZERO) && (ys[i] * alphas[i] > -IS_ZERO)) {
bSum += ys[i] - sum[i];
bCounter++;
}
}
if (bCounter == 0) {
// even unlikelier
bSum = 0.0d;
for (int i = 0; i < alphas.length; i++) {
bSum += ys[i] - sum[i];
bCounter++;
}
}
}
return new EvoSVMModel(exampleSet, supportVectors, kernel, bSum / bCounter);
}
}