/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
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* http://rapid-i.com
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* 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,
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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package com.rapidminer.operator.features.weighting;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Statistics;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.IOContainer;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorCreationException;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.learner.functions.kernel.AbstractMySVMLearner;
import com.rapidminer.operator.learner.functions.kernel.JMySVMLearner;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.OperatorService;
/**
* Uses the coefficients of the normal vector of a linear SVM as feature weights.
* In contrast to most of the SVM based operators available in RapidMiner, this one works
* for multiple classes, too.
*
* @author Ingo Mierswa
* @version $Id: SVMWeighting.java,v 1.9 2008/05/28 10:52:03 ingomierswa Exp $
*
*/
public class SVMWeighting extends AbstractWeighting {
public SVMWeighting(OperatorDescription description) {
super(description);
}
public AttributeWeights calculateWeights(ExampleSet exampleSet) throws OperatorException {
// checks
Attribute label = exampleSet.getAttributes().getLabel();
if (label == null) {
throw new UserError(this, 105);
}
// create and init SVM operator
Operator svmOperator = null;
try {
svmOperator = OperatorService.createOperator(JMySVMLearner.class);
} catch (OperatorCreationException e) {
throw new UserError(this, 904, "inner pca operator", e.getMessage());
}
svmOperator.setParameter(AbstractMySVMLearner.PARAMETER_KERNEL_TYPE, AbstractMySVMLearner.KERNEL_DOT + "");
svmOperator.setParameter(AbstractMySVMLearner.PARAMETER_C, getParameterAsDouble(AbstractMySVMLearner.PARAMETER_C) + "");
svmOperator.setParameter(AbstractMySVMLearner.PARAMETER_CALCULATE_WEIGHTS, "true");
// calculate weights
AttributeWeights result = null;
// regression or binomial case
if (label.isNumerical() || (label.isNominal() && (label.getMapping().size() == 2))) {
result = calculateAttributeWeights(svmOperator, exampleSet);
} else if (label.isNominal()) {
// polynomial case
exampleSet.recalculateAttributeStatistics(label);
int totalClassSizeSum = 0;
int[] classFrequencies = new int[label.getMapping().size()];
int counter = 0;
List<AttributeWeights> allWeights = new LinkedList<AttributeWeights>();
for (String value : label.getMapping().getValues()) {
int frequency = (int)exampleSet.getStatistics(label, Statistics.COUNT, value);
classFrequencies[counter++] = frequency;
totalClassSizeSum += frequency;
// create temp label (one vs. all)
Attribute tempLabel = AttributeFactory.createAttribute("temp_label", Ontology.BINOMINAL);
int positiveIndex = tempLabel.getMapping().mapString("positive");
int negativeIndex = tempLabel.getMapping().mapString("negative");
exampleSet.getExampleTable().addAttribute(tempLabel);
exampleSet.getAttributes().addRegular(tempLabel);
int currentLabelIndex = label.getMapping().mapString(value);
for (Example e : exampleSet) {
int oldLabelValue = (int)e.getValue(label);
if (oldLabelValue == currentLabelIndex) {
e.setValue(tempLabel, positiveIndex);
} else {
e.setValue(tempLabel, negativeIndex);
}
}
exampleSet.getAttributes().remove(tempLabel);
exampleSet.getAttributes().setLabel(tempLabel);
// actual calculating weights for this class
AttributeWeights currentWeights = calculateAttributeWeights(svmOperator, exampleSet);
allWeights.add(currentWeights);
// clean up
exampleSet.getAttributes().setLabel(label);
exampleSet.getExampleTable().removeAttribute(tempLabel);
}
// build the weighted average for all weights
result = new AttributeWeights();
Iterator<String> nameIterator = allWeights.get(0).getAttributeNames().iterator();
while (nameIterator.hasNext()) {
String attributeName = nameIterator.next();
double currentWeightSum = 0.0d;
counter = 0;
for (AttributeWeights weights : allWeights) {
double weight = weights.getWeight(attributeName);
currentWeightSum += Math.abs(weight) * classFrequencies[counter++];
}
result.setWeight(attributeName, currentWeightSum / totalClassSizeSum);
}
} else {
logError("Calculation of SVM weights only possible for numerical or nominal labels.");
}
result.setSource(this.getName());
return result;
}
private AttributeWeights calculateAttributeWeights(Operator svmOperator, ExampleSet exampleSet) throws OperatorException {
IOContainer ioContainer = new IOContainer(exampleSet);
ioContainer = svmOperator.apply(ioContainer);
AttributeWeights result = ioContainer.remove(AttributeWeights.class);
return result;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeDouble(AbstractMySVMLearner.PARAMETER_C, "The SVM complexity weighting factor.", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0d));
return types;
}
}