/*
* 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.performance.cost;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.ValueDouble;
import com.rapidminer.operator.performance.MeasuredPerformance;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeEnumeration;
import com.rapidminer.parameter.ParameterTypeMatrix;
import com.rapidminer.parameter.ParameterTypeString;
import com.rapidminer.tools.Ontology;
/**
* 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
*/
public class CostEvaluator extends Operator {
private static final String PARAMETER_COST_MATRIX = "cost_matrix";
private static final String PARAMETER_KEEP_EXAMPLE_SET = "keep_exampleSet";
private static final String PARAMETER_CLASS_NAME = "class_name";
private static final String PARAMETER_CLASS_DEFINITION = "class_order_definition";
private InputPort exampleSetInput = getInputPorts().createPort("example set");
private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
private OutputPort performanceOutput = getOutputPorts().createPort("performance");
private double lastCosts = Double.NaN;
public CostEvaluator(OperatorDescription description) {
super(description);
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, Ontology.ATTRIBUTE_VALUE, Attributes.LABEL_NAME));
getTransformer().addGenerationRule(performanceOutput, PerformanceVector.class);
getTransformer().addPassThroughRule(exampleSetInput, exampleSetOutput);
addValue(new ValueDouble("costs", "The last costs.") {
@Override
public double getDoubleValue() {
return lastCosts;
}
});
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData();
Attribute label = exampleSet.getAttributes().getLabel();
if (label != null) {
if (label.isNominal()) {
double[][] costMatrix = getParameterAsMatrix(PARAMETER_COST_MATRIX);
// build label ordering map
Map<String, Integer> classOrderMap = null;
if (isParameterSet(PARAMETER_CLASS_DEFINITION)) {
String[] enumeratedValues = ParameterTypeEnumeration.transformString2Enumeration(getParameterAsString(PARAMETER_CLASS_DEFINITION));
if (enumeratedValues.length > 0) {
classOrderMap = new HashMap<String, Integer>();
int i = 0;
for (String className : enumeratedValues) {
classOrderMap.put(className, i);
i++;
}
// check whether each possible label occurred once
for (String value : label.getMapping().getValues()) {
if (!classOrderMap.containsKey(value)) {
throw new UserError(this, "performance_costs.class_order_definition_misses_value", value);
}
}
// check whether map is of same size than costMatrix
if (costMatrix.length != classOrderMap.size())
throw new UserError(this, "performance_costs.cost_matrix_with_wrong_dimension", costMatrix.length, classOrderMap.size());
}
}
MeasuredPerformance criterion = new ClassificationCostCriterion(costMatrix, classOrderMap, 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);
}
// setting logging value
lastCosts = criterion.getAverage();
exampleSetOutput.deliver(exampleSet);
performanceOutput.deliver(performance);
} else {
throw new UserError(this, 101, "CostEvaluator", label.getName());
}
} else {
throw new UserError(this, 105);
}
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeBoolean(PARAMETER_KEEP_EXAMPLE_SET, "Indicates if the example set should be kept.", false);
type.setHidden(true);
types.add(type);
types.add(new ParameterTypeMatrix(PARAMETER_COST_MATRIX, "The matrix of missclassification costs. Columns and Rows in order of internal mapping.", "Cost Matrix", "Predicted Class", "True Class", true, false));
types.add(new ParameterTypeEnumeration(PARAMETER_CLASS_DEFINITION, "With this parameter it is possible to define the order of classes used in the cost matrix. First class in this list is First class in the matrix.",
new ParameterTypeString(PARAMETER_CLASS_NAME, "The name of the class."), false));
return types;
}
}