/*
* 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.features.weighting;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.learner.tree.Criterion;
import com.rapidminer.operator.learner.tree.NumericalSplitter;
/**
* This operator calculates the relevance of a feature by computing the
* an entropy value of the class distribution, if the given example set would
* have been splitted according to the feature.
*
* @author Ingo Mierswa
* @version $Id: AbstractEntropyWeighting.java,v 1.6 2008/05/09 19:23:22 ingomierswa Exp $
*/
public abstract class AbstractEntropyWeighting extends AbstractWeighting {
public AbstractEntropyWeighting(OperatorDescription description) {
super(description);
}
public abstract Criterion getEntropyCriterion();
public AttributeWeights calculateWeights(ExampleSet exampleSet) throws OperatorException {
Attribute label = exampleSet.getAttributes().getLabel();
if (!label.isNominal()) {
throw new UserError(this, 101, getName(), label.getName());
}
// calculate the actual infogain values and assign them to weights
Criterion criterion = getEntropyCriterion();
NumericalSplitter splitter = new NumericalSplitter(criterion);
AttributeWeights weights = new AttributeWeights(exampleSet);
for (Attribute attribute : exampleSet.getAttributes()) {
SplittedExampleSet splitted = null;
if (attribute.isNominal()) {
splitted = SplittedExampleSet.splitByAttribute(exampleSet, attribute);
double weight = criterion.getBenefit(splitted);
weights.setWeight(attribute.getName(), weight);
} else {
double splitValue = splitter.getBestSplit(exampleSet, attribute);
splitted = SplittedExampleSet.splitByAttribute(exampleSet, attribute, splitValue);
double weight = criterion.getBenefit(splitted);
weights.setWeight(attribute.getName(), weight);
}
}
return weights;
}
}