/*
* 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.learner.lazy;
import java.util.HashMap;
import java.util.Map;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.HeaderExampleSet;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.learner.meta.Vote;
import com.rapidminer.tools.Tools;
/**
* Average model simply calculates the average of the attributes as prediction.
* For classification problems the mode of all attribute values is returned.
* This model is mainly used in meta learning schemes (like {@link Vote}.
* Please keep in mind that each attribute available on creation time is used
* for calculating the outcome.
*
* @author Ingo Mierswa
*/
public class AttributeBasedVotingModel extends PredictionModel {
private static final long serialVersionUID = -8814468417883548971L;
private double majorityVote;
public AttributeBasedVotingModel(ExampleSet exampleSet, double majorityVote) {
super(exampleSet);
this.majorityVote = majorityVote;
}
@Override
public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) throws OperatorException {
for (Example example : exampleSet) {
if (predictedLabelAttribute.isNominal()) {
// classification
Map<String, Double> counter = new HashMap<String, Double>();
for (Attribute attribute : example.getAttributes()) {
if (!attribute.isNominal())
throw new UserError(null, 103, "nominal voting");
String labelValue = attribute.getMapping().mapIndex((int)example.getValue(attribute));
double labelSum = 0.0d;
if (counter.get(labelValue) != null) {
labelSum = counter.get(labelValue);
}
labelSum += 1.0d;
counter.put(labelValue, labelSum);
}
// calculate confidences and best class
String bestClass = null;
double best = Double.NEGATIVE_INFINITY;
for (String labelValue : getLabel().getMapping().getValues()) {
Double sumObject = counter.get(labelValue);
if (sumObject == null) {
example.setConfidence(labelValue, 0.0d);
} else {
example.setConfidence(labelValue, sumObject / exampleSet.getAttributes().size());
if (sumObject > best) {
best = sumObject;
bestClass = labelValue;
}
}
}
// set crisp prediction
if (bestClass != null) {
example.setPredictedLabel(predictedLabelAttribute.getMapping().mapString(bestClass));
} else {
example.setPredictedLabel(majorityVote);
}
} else {
// regression
double average = 0.0d;
for (Attribute attribute : example.getAttributes()) {
average += example.getValue(attribute);
}
average /= example.getAttributes().size();
example.setValue(predictedLabelAttribute, average);
}
}
return exampleSet;
}
@Override
public String toString() {
StringBuffer buffer = new StringBuffer();
HeaderExampleSet header = getTrainingHeader();
Attribute label = header.getAttributes().getLabel();
if (label.isNominal())
buffer.append("Using the majority of the following attributes for prediction:" + Tools.getLineSeparator());
else
buffer.append("Using the avarage of the following attributes for prediction:" + Tools.getLineSeparator());
for (Attribute attribute: header.getAttributes()) {
if (attribute.isNominal() && label.isNominal() || attribute.isNumerical() && label.isNumerical()) {
buffer.append(" " + attribute.getName() + Tools.getLineSeparator());
}
}
buffer.append(Tools.getLineSeparator());
if (label.isNominal())
buffer.append("The default value is " + label.getMapping().mapIndex((int)majorityVote));
return buffer.toString();
}
}