/*
* 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.meta;
import java.awt.Component;
import java.util.Iterator;
import java.util.List;
import javax.swing.JTabbedPane;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.gui.tools.ExtendedJTabbedPane;
import com.rapidminer.operator.IOContainer;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.Tools;
/**
* A model for the RapidMiner AdaBoost implementation.
*
* @author Martin Scholz
* @version $Id: AdaBoostModel.java,v 1.6 2008/05/09 19:22:48 ingomierswa Exp $
*/
public class AdaBoostModel extends PredictionModel {
private static final long serialVersionUID = -4145136493164813582L;
// Holds the models
private List<Model> models;
// Holds the weights
private List<Double> weights;
// If set to a value i >= 0 then only the first i models are applied
private int maxModelNumber = -1;
private static final String MAX_MODEL_NUMBER = "iteration";
public AdaBoostModel(ExampleSet exampleSet, List<Model> models, List<Double> weights) {
super(exampleSet);
this.models = models;
this.weights = weights;
for (double i: weights) {
if (Double.isNaN(i) || Double.isInfinite(i)) {
logWarning("Found model weight " + i);
}
}
}
/**
* Setting the parameter <code>MAX_MODEL_NUMBER</code> allows to discard
* all but the first n models for specified n.
*/
public void setParameter(String name, String value) throws OperatorException {
if (name.equalsIgnoreCase(MAX_MODEL_NUMBER)) {
try {
this.maxModelNumber = Integer.parseInt(value);
return;
}
catch (NumberFormatException e) {}
}
super.setParameter(name, value);
}
/**
* Using this setter with a positive value makes the model discard all
* but the specified number of base models. A value of -1 turns off this
* option.
*/
public void setMaxModelNumber(int numModels) {
this.maxModelNumber = numModels;
}
public Component getVisualizationComponent(IOContainer container) {
JTabbedPane tabPane = new ExtendedJTabbedPane();
for (int i = 0; i < this.getNumberOfModels(); i++) {
Model model = this.getModel(i);
tabPane.add("Model " + (i + 1) + " [w = " + Tools.formatNumber(getWeightForModel(i)) + "]", model.getVisualizationComponent(container));
}
return tabPane;
}
/** @return a <code>String</code> representation of this boosting model. */
public String toString() {
StringBuffer result = new StringBuffer(super.toString() + Tools.getLineSeparator() + "Number of inner models: " + this.getNumberOfModels() + Tools.getLineSeparators(2));
for (int i = 0; i < this.getNumberOfModels(); i++) {
Model model = this.getModel(i);
result.append((i > 0 ? Tools.getLineSeparator() : "") + "Embedded model #" + i + " (weight: " + Tools.formatNumber(this.getWeightForModel(i)) + "): "
+ Tools.getLineSeparator() + model.toResultString());
}
return result.toString();
}
/** @return the number of embedded models */
public int getNumberOfModels() {
if (this.maxModelNumber >= 0) {
return Math.min(this.maxModelNumber, this.models.size());
}
else {
return this.models.size();
}
}
private double getWeightForModel(int modelNr) {
return this.weights.get(modelNr);
}
/**
* Getter method for embedded models
*
* @param index
* the number of a model part of this boost model
* @return binary or nominal decision model
*/
public Model getModel(int index) {
return this.models.get(index);
}
/**
* Iterates over all models and returns the class with maximum likelihood.
*
* @param origExampleSet
* the set of examples to be classified
*/
public ExampleSet performPrediction(ExampleSet origExampleSet, Attribute predictedLabel) throws OperatorException {
final String attributePrefix = "AdaBoostModelPrediction";
final int numLabels = predictedLabel.getMapping().size();
final Attribute[] specialAttributes = new Attribute[numLabels];
for (int i = 0; i < numLabels; i++) {
specialAttributes[i] =
com.rapidminer.example.Tools.createSpecialAttribute(origExampleSet, attributePrefix + i, Ontology.NUMERICAL);
}
Iterator<Example> reader = origExampleSet.iterator();
while (reader.hasNext()) {
Example example = reader.next();
for (int i = 0; i < specialAttributes.length; i++) {
example.setValue(specialAttributes[i], 0);
}
}
reader = origExampleSet.iterator();
for (int modelNr = 0; modelNr < this.getNumberOfModels(); modelNr++) {
Model model = this.getModel(modelNr);
ExampleSet exampleSet = (ExampleSet) origExampleSet.clone();
exampleSet = model.apply(exampleSet);
this.updateEstimates(exampleSet, modelNr, specialAttributes);
PredictionModel.removePredictedLabel(exampleSet);
}
// Turn prediction weights into confidences and a crisp predcition:
this.evaluateSpecialAttributes(origExampleSet, specialAttributes);
// Clean up attributes:
for (int i = 0; i < numLabels; i++) {
origExampleSet.getAttributes().remove(specialAttributes[i]);
origExampleSet.getExampleTable().removeAttribute(specialAttributes[i]);
}
return origExampleSet;
}
private void updateEstimates(ExampleSet exampleSet, int modelNr, Attribute[] specialAttributes) {
Iterator<Example> reader = exampleSet.iterator();
while (reader.hasNext()) {
Example example = reader.next();
int predicted = (int) example.getPredictedLabel();
double oldValue = example.getValue(specialAttributes[predicted]);
if (Double.isNaN(oldValue)) {
logWarning("Found NaN confidence as intermediate prediction.");
oldValue = 0;
}
if ( ! Double.isInfinite(oldValue)) {
example.setValue(specialAttributes[predicted], oldValue + this.getWeightForModel(modelNr));
}
}
}
private void evaluateSpecialAttributes(ExampleSet exampleSet, Attribute[] specialAttributes) {
Attribute exSetLabel = exampleSet.getAttributes().getLabel();
Iterator<Example> reader = exampleSet.iterator();
while (reader.hasNext()) {
Example example = reader.next();
double sum = 0;
double[] confidences = new double[specialAttributes.length];
double bestConf = -1;
int bestLabel = 0;
for (int n = 0; n < confidences.length; n++) {
confidences[n] = example.getValue(specialAttributes[n]);
if (confidences[n] > bestConf) {
bestConf = confidences[n];
bestLabel = n;
}
}
example.setValue(example.getAttributes().getPredictedLabel(), exSetLabel.getMapping().mapString(this.getLabel().getMapping().mapIndex(bestLabel)));
for (int n = 0; n < confidences.length; n++) {
confidences[n] = Math.exp(confidences[n] - bestConf);
// remember for normalization:
sum += confidences[n];
}
// Normalize and set confidence values for all classes:
if (Double.isInfinite(sum) || Double.isNaN(sum)) {
int best = (int) example.getPredictedLabel();
for (int k = 0; k < confidences.length; k++) {
confidences[k] = 0;
}
confidences[best] = 1;
}
else {
for (int k = 0; k < confidences.length; k++) {
confidences[k] /= sum;
example.setConfidence(exampleSet.getAttributes().getLabel().getMapping().mapIndex(k), confidences[k]);
}
}
}
}
}