/*
* 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.performance;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.Tools;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.LogService;
/**
* <p>This performance evaluator operator should be used for classification tasks,
* i.e. in cases where the label attribute has a binominal value type.
* Other polynominal classification tasks, i.e. tasks with more than two classes
* can be handled by the {@link PolynominalClassificationPerformanceEvaluator} operator.
* This operator expects a test {@link ExampleSet}
* as input, whose elements have both true and predicted labels, and delivers as
* output a list of performance values according to a list of performance
* criteria that it calculates. If an input performance vector was already
* given, this is used for keeping the performance values.</p>
*
* <p>All of the performance criteria can be switched on using boolean parameters.
* Their values can be queried by a ProcessLogOperator using the same names.
* The main criterion is used for comparisons and need to be specified only for
* processes where performance vectors are compared, e.g. feature selection
* or other meta optimization process setups.
* If no other main criterion was selected, the first criterion in the
* resulting performance vector will be assumed to be the main criterion.</p>
*
* <p>The resulting performance vectors are usually compared with a standard
* performance comparator which only compares the fitness values of the main
* criterion. Other implementations than this simple comparator can be
* specified using the parameter <var>comparator_class</var>. This may for
* instance be useful if you want to compare performance vectors according to
* the weighted sum of the individual criteria. In order to implement your own
* comparator, simply subclass {@link PerformanceComparator}. Please note that
* for true multi-objective optimization usually another selection scheme is
* used instead of simply replacing the performance comparator.</p>
*
* @author Ingo Mierswa
* @version $Id: BinominalClassificationPerformanceEvaluator.java,v 1.5 2008/05/09 19:22:43 ingomierswa Exp $
*/
public class BinominalClassificationPerformanceEvaluator extends AbstractPerformanceEvaluator {
/** The proper criteria to the names. */
private static final Class[] SIMPLE_CRITERIA_CLASSES = {
com.rapidminer.operator.performance.AreaUnderCurve.class
};
public BinominalClassificationPerformanceEvaluator(OperatorDescription description) {
super(description);
}
protected void checkCompatibility(ExampleSet exampleSet) throws OperatorException {
Tools.isLabelled(exampleSet);
Tools.isNonEmpty(exampleSet);
Attribute label = exampleSet.getAttributes().getLabel();
if (!label.isNominal()) {
throw new UserError(this, 101, "the calculation of performance criteria for binominal classification tasks", label.getName());
}
if (label.getMapping().size() != 2) {
throw new UserError(this, 114, "the calculation of performance criteria for binominal classification tasks", label.getName());
}
}
/** Returns null. */
protected double[] getClassWeights(Attribute label) throws UndefinedParameterError {
return null;
}
public List<PerformanceCriterion> getCriteria() {
List<PerformanceCriterion> performanceCriteria = new LinkedList<PerformanceCriterion>();
for (int i = 0; i < SIMPLE_CRITERIA_CLASSES.length; i++) {
try {
performanceCriteria.add((PerformanceCriterion)SIMPLE_CRITERIA_CLASSES[i].newInstance());
} catch (InstantiationException e) {
LogService.getGlobal().logError("Cannot instantiate " + SIMPLE_CRITERIA_CLASSES[i] + ". Skipping...");
} catch (IllegalAccessException e) {
LogService.getGlobal().logError("Cannot instantiate " + SIMPLE_CRITERIA_CLASSES[i] + ". Skipping...");
}
}
// binary classification criteria
for (int i = 0; i < BinaryClassificationPerformance.NAMES.length; i++) {
performanceCriteria.add(new BinaryClassificationPerformance(i));
}
return performanceCriteria;
}
}