/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.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 java.util.logging.Level;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
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;
/**
* <p>
* A performance evaluator is an operator that 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 processes. 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>
*
* <p>
* Additional user-defined implementations of {@link PerformanceCriterion} can be specified by using
* the parameter list <var>additional_performance_criteria</var>. Each key/value pair in this list
* must specify a fully qualified classname (as the key), and a string parameter (as value) that is
* passed to the constructor. Please make sure that the class files are in the classpath (this is
* the case if the implementations are supplied by a plugin) and that they implement a one-argument
* constructor taking a string parameter. It must also be ensured that these classes extend
* {@link MeasuredPerformance} since the PerformanceEvaluator operator will only support these
* criteria. Please note that only the first three user defined criteria can be used as logging
* value with names "user1", ... , "user3".
* </p>
*
* @author Ingo Mierswa
*/
public class PerformanceEvaluator 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.RootMeanSquaredError.class,
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.RootRelativeSquaredError.class,
com.rapidminer.operator.performance.SquaredError.class,
com.rapidminer.operator.performance.CorrelationCriterion.class,
com.rapidminer.operator.performance.SquaredCorrelationCriterion.class,
com.rapidminer.operator.performance.PredictionAverage.class,
com.rapidminer.operator.performance.AreaUnderCurve.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 PerformanceEvaluator(OperatorDescription description) {
super(description);
}
/** Does nothing. */
@Override
protected void checkCompatibility(ExampleSet exampleSet) throws OperatorException {}
@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>();
// simple criteria
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...");
LogService
.getRoot()
.log(Level.SEVERE,
"com.rapidminer.operator.performance.PerformanceEvaluator.instantiating_simple_criteria_classes_error",
SIMPLE_CRITERIA_CLASSES[i]);
} catch (IllegalAccessException e) {
// LogService.getGlobal().logError("Cannot instantiate " +
// SIMPLE_CRITERIA_CLASSES[i] + ". Skipping...");
LogService
.getRoot()
.log(Level.SEVERE,
"com.rapidminer.operator.performance.PerformanceEvaluator.instantiating_simple_criteria_classes_error",
SIMPLE_CRITERIA_CLASSES[i]);
}
}
// 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));
}
return performanceCriteria;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeList(PARAMETER_CLASS_WEIGHTS,
"The weights for all classes, 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 true;
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
switch (capability) {
case NUMERICAL_LABEL:
case BINOMINAL_LABEL:
case POLYNOMINAL_LABEL:
case ONE_CLASS_LABEL:
return true;
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;
}
}
}