/*
* 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.cost;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.performance.MeasuredPerformance;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeMatrix;
/**
* This operator provides the ability to evaluate classification costs.
* Therefore a cost matrix might be specified, denoting the costs for every
* possible classification outcome: predicted label x real label.
* Costs will be minimized during optimization.
*
* @author Sebastian Land
* @version $Id: CostEvaluator.java,v 1.5 2008/07/13 11:00:58 ingomierswa Exp $
*/
public class CostEvaluator extends Operator {
public CostEvaluator(OperatorDescription description) {
super(description);
}
private static final String PARAMETER_COST_MATRIX = "cost_matrix";
private static final String PARAMETER_KEEP_EXAMPLE_SET = "keep_exampleSet";
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = getInput(ExampleSet.class);
Attribute label = exampleSet.getAttributes().getLabel();
if (label != null) {
if (label.isNominal()) {
double[][] costMatrix = getParameterAsMatrix(PARAMETER_COST_MATRIX);
MeasuredPerformance criterion = new ClassificationCostCriterion(costMatrix, label, exampleSet.getAttributes().getPredictedLabel());
PerformanceVector performance = new PerformanceVector();
performance.addCriterion(criterion);
// now measuring costs
criterion.startCounting(exampleSet, false);
for (Example example: exampleSet) {
criterion.countExample(example);
}
if (getParameterAsBoolean(PARAMETER_KEEP_EXAMPLE_SET)) {
return new IOObject[] {exampleSet, performance};
} else {
return new IOObject[] {performance};
}
}
}
return new IOObject[] {exampleSet};
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeBoolean(PARAMETER_KEEP_EXAMPLE_SET, "Indicates if the example set should be kept.", false));
types.add(new ParameterTypeMatrix(PARAMETER_COST_MATRIX, "The cost matrix in Matlab single line format", "Cost Matrix", "Predicted Class", "True Class", true, false));
return types;
}
public Class<?>[] getInputClasses() {
return new Class[] { ExampleSet.class };
}
public Class<?>[] getOutputClasses() {
return new Class[] { PerformanceVector.class };
}
}