/*
* 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;
import java.util.Iterator;
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.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeList;
import com.rapidminer.parameter.ParameterTypeString;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.LogService;
import com.rapidminer.tools.Ontology;
/**
* <p>This performance evaluator operator should be used for classification tasks,
* i.e. in cases where the label attribute has a (poly-)nominal value type.
* The 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
*/
public class PolynominalClassificationPerformanceEvaluator extends AbstractPerformanceEvaluator {
/** The parameter name for "The weights for all classes (first column: class name, second column: weight), empty: using 1 for all classes." */
public static final String PARAMETER_CLASS_WEIGHTS = "class_weights";
/** The proper criteria to the names. */
private static final Class[] SIMPLE_CRITERIA_CLASSES = {
com.rapidminer.operator.performance.AbsoluteError.class,
com.rapidminer.operator.performance.RelativeError.class,
com.rapidminer.operator.performance.LenientRelativeError.class,
com.rapidminer.operator.performance.StrictRelativeError.class,
com.rapidminer.operator.performance.NormalizedAbsoluteError.class,
com.rapidminer.operator.performance.RootMeanSquaredError.class,
com.rapidminer.operator.performance.RootRelativeSquaredError.class,
com.rapidminer.operator.performance.SquaredError.class,
com.rapidminer.operator.performance.CorrelationCriterion.class,
com.rapidminer.operator.performance.SquaredCorrelationCriterion.class,
com.rapidminer.operator.performance.CrossEntropy.class,
com.rapidminer.operator.performance.Margin.class,
com.rapidminer.operator.performance.SoftMarginLoss.class,
com.rapidminer.operator.performance.LogisticLoss.class
};
public PolynominalClassificationPerformanceEvaluator(OperatorDescription description) {
super(description);
}
@Override
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 classification tasks", label.getName());
}
}
@Override
protected double[] getClassWeights(Attribute label) throws UndefinedParameterError {
double[] weights = null;
if (isParameterSet(PARAMETER_CLASS_WEIGHTS)) {
weights = new double[label.getMapping().size()];
for (int i = 0; i < weights.length; i++) {
weights[i] = 1.0d;
}
List<String[]> classWeights = getParameterList(PARAMETER_CLASS_WEIGHTS);
Iterator<String[]> i = classWeights.iterator();
while (i.hasNext()) {
String[] classWeightArray = i.next();
String className = classWeightArray[0];
double classWeight = Double.valueOf(classWeightArray[1]);
int index = label.getMapping().mapString(className);
weights[index] = classWeight;
}
// logging
List<Double> weightList = new LinkedList<Double>();
for (double d : weights)
weightList.add(d);
log(getName() + ": used class weights --> " + weightList);
}
return weights;
}
@Override
public List<PerformanceCriterion> getCriteria() {
List<PerformanceCriterion> performanceCriteria = new LinkedList<PerformanceCriterion>();
// multi class classification criteria
for (int i = 0; i < MultiClassificationPerformance.NAMES.length; i++) {
performanceCriteria.add(new MultiClassificationPerformance(i));
}
// multi class classification criteria
for (int i = 0; i < WeightedMultiClassPerformance.NAMES.length; i++) {
performanceCriteria.add(new WeightedMultiClassPerformance(i));
}
// rank correlation criteria
for (int i = 0; i < RankCorrelation.NAMES.length; i++) {
performanceCriteria.add(new RankCorrelation(i));
}
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...");
}
}
return performanceCriteria;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeList(PARAMETER_CLASS_WEIGHTS, "The weights for all classes (first column: class name, second column: weight), empty: using 1 for all classes.",
new ParameterTypeString("class_name", "The name of the class."),
new ParameterTypeDouble("weight", "The weight for this class.", 0.0d, Double.POSITIVE_INFINITY, 1.0d)));
return types;
}
@Override
protected boolean canEvaluate(int valueType) {
return Ontology.ATTRIBUTE_VALUE_TYPE.isA(valueType, Ontology.NOMINAL);
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
switch (capability) {
case BINOMINAL_LABEL:
case POLYNOMINAL_LABEL:
return true;
case NUMERICAL_LABEL:
case ONE_CLASS_LABEL:
return false;
case POLYNOMINAL_ATTRIBUTES:
case BINOMINAL_ATTRIBUTES:
case NUMERICAL_ATTRIBUTES:
case WEIGHTED_EXAMPLES:
case MISSING_VALUES:
return true;
case NO_LABEL:
case UPDATABLE:
case FORMULA_PROVIDER:
default:
return false;
}
}
}