/*
* 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.HashMap;
import java.util.Iterator;
import javax.swing.JTabbedPane;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
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;
/**
* This class is associated to the MetaCost operator and supports the
* evaluation procedures of the MetaCost method.
*
* @author Helge Homburg
* @version $Id: MetaCostModel.java,v 1.5 2008/05/09 19:22:47 ingomierswa Exp $
*/
public class MetaCostModel extends PredictionModel {
private static final long serialVersionUID = -7378871544357578954L;
private Model[] models;
private double[][] costMatrix;
public MetaCostModel(ExampleSet exampleSet, Model[] models, double[][] costMatrix) {
super(exampleSet);
this.models = models;
this.costMatrix = costMatrix;
}
public int getNumberOfModels() {
return models.length;
}
/** Returns a binary decision model for the given classification index. */
public Model getModel(int index) {
return models[index];
}
/** Returns a single value from the cost matrix. */
public double getCostValue(int i, int j) {
return costMatrix[i][j];
}
public ExampleSet performPrediction(ExampleSet originalExampleSet, Attribute predictedLabel) throws OperatorException {
ExampleSet exampleSet = (ExampleSet)originalExampleSet.clone();
int numberOfClasses = getLabel().getMapping().getValues().size();
double[][] confidences = new double[exampleSet.size()][numberOfClasses];
// Hash maps are used for addressing particular class values using indices without relying
// upon a consistent index distribution of the corresponding substructure.
int currentNumber = 0;
HashMap<Integer, String> classIndexMap = new HashMap<Integer, String> (numberOfClasses);
for (String currentClass : getLabel().getMapping().getValues()) {
classIndexMap.put(currentNumber, currentClass);
currentNumber++;
}
// 1. Iterate over all models and all examples for every model to receive all confidence values.
for (int k = 0; k < getNumberOfModels(); k++) {
Model model = getModel(k);
exampleSet = model.apply(exampleSet);
Iterator<Example> reader = exampleSet.iterator();
int counter = 0;
while (reader.hasNext()) {
Example example = reader.next();
int currentClassNumber = 0;
for (String currentClass : getLabel().getMapping().getValues()) {
confidences[counter][currentClassNumber] += example.getConfidence(currentClass);
currentClassNumber++;
}
counter++;
}
PredictionModel.removePredictedLabel(exampleSet);
}
// 2. Iterate again over all examples to compute a prediction and a confidence distribution for
// all examples depending on the results of step 1 and the cost matrix.
Attribute classificationCost = AttributeFactory.createAttribute(Attributes.CLASSIFICATION_COST, Ontology.REAL);
originalExampleSet.getExampleTable().addAttribute(classificationCost);
originalExampleSet.getAttributes().setCost(classificationCost);
Attribute classLabel = originalExampleSet.getAttributes().getLabel();
boolean hasLabel = (classLabel != null);
Iterator<Example> reader = originalExampleSet.iterator();
int counter = 0;
while (reader.hasNext()) {
Example example = reader.next();
for (int i = 0; i < numberOfClasses; i++) {
confidences[counter][i] = confidences[counter][i] / getNumberOfModels();
}
double[] conditionalRisk = new double[numberOfClasses];
int bestIndex = - 1;
double bestValue = Double.POSITIVE_INFINITY;
for (int i = 0; i < numberOfClasses; i++) {
for (int j = 0; j < numberOfClasses; j++) {
conditionalRisk[i] += confidences[counter][j] * costMatrix[i][j];
}
if (conditionalRisk[i] < bestValue) {
bestValue = conditionalRisk[i];
bestIndex = i;
}
}
example.setPredictedLabel(getLabel().getMapping().mapString(classIndexMap.get(bestIndex)));
if (hasLabel) {
int labelIndex = getLabel().getMapping().mapString(classIndexMap.get((int)example.getLabel()));
example.setValue(originalExampleSet.getAttributes().getCost(), costMatrix[bestIndex][labelIndex]);
} else {
example.setValue(originalExampleSet.getAttributes().getCost(), conditionalRisk[bestIndex]);
}
for (int i = 0; i < numberOfClasses; i++) {
example.setConfidence(classIndexMap.get(i), confidences[counter][i]);
}
counter++;
}
return originalExampleSet;
}
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), model.getVisualizationComponent(container));
}
return tabPane;
}
public String toString() {
StringBuffer result = new StringBuffer(super.toString() + Tools.getLineSeparator());
for (int i = 0; i < models.length; i++)
result.append((i > 0 ? Tools.getLineSeparator() : "") + models[i].toString());
return result.toString();
}
}