/**
* 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.LinkedList;
import java.util.List;
import java.util.logging.Level;
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.tools.LogService;
import com.rapidminer.tools.Ontology;
/**
* <p>
* This performance evaluator operator should be used for regression tasks, i.e. in cases where the
* label attribute has a numerical 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 RegressionPerformanceEvaluator extends AbstractPerformanceEvaluator {
/** 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 };
public RegressionPerformanceEvaluator(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.isNumerical()) {
throw new UserError(this, 102, "the calculation of performance criteria for regression tasks", label.getName());
}
}
@Override
protected double[] getClassWeights(Attribute label) {
return null;
}
@Override
public List<PerformanceCriterion> getCriteria() {
List<PerformanceCriterion> allCriteria = new LinkedList<PerformanceCriterion>();
for (int i = 0; i < SIMPLE_CRITERIA_CLASSES.length; i++) {
try {
allCriteria.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.RegressionPerformanceEvaluator.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.RegressionPerformanceEvaluator.instantiating_simple_criteria_classes_error",
SIMPLE_CRITERIA_CLASSES[i]);
}
}
// rank correlation criteria
for (int i = 0; i < RankCorrelation.NAMES.length; i++) {
allCriteria.add(new RankCorrelation(i));
}
return allCriteria;
}
@Override
protected boolean canEvaluate(int valueType) {
return Ontology.ATTRIBUTE_VALUE_TYPE.isA(valueType, Ontology.NUMERICAL);
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
switch (capability) {
case NUMERICAL_LABEL:
return true;
case BINOMINAL_LABEL:
case POLYNOMINAL_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;
}
}
}