/*
* 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.validation;
import java.util.ArrayList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.operator.IOContainer;
import com.rapidminer.operator.IOObject;
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.PerformanceVector;
import com.rapidminer.operator.visualization.ProcessLogOperator;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.tools.math.AverageVector;
/**
* <p>
* <code>BatchXValidation</code> encapsulates a cross-validation process. The
* example set {@rapidminer.math S} is split up into <var> number_of_validations</var>
* subsets {@rapidminer.math S_i}. The inner operators are applied
* <var>number_of_validations</var> times using {@rapidminer.math S_i} as the test
* set (input of the second inner operator) and {@rapidminer.math S\backslash S_i}
* training set (input of the first inner operator).
* </p>
*
* <p>In contrast to the usual cross validation operator (see {@link XValidation})
* this operator does not (randomly) split the data itself but uses the partition
* defined by the special attribute "batch". This can be an arbitrary
* nominal or integer attribute where each possible value occurs at least once
* (since many learning schemes depend on this minimum number of examples).
* </p>
*
* <p>
* The first inner operator must accept an
* {@link com.rapidminer.example.ExampleSet} while the second must accept an
* {@link com.rapidminer.example.ExampleSet} and the output of the first (which
* is in most cases a {@link com.rapidminer.operator.Model}) and must produce
* a {@link com.rapidminer.operator.performance.PerformanceVector}.
* </p>
*
* <p>The cross validation operator provides several values which can be logged
* by means of a {@link ProcessLogOperator}. Of course the number of the current
* iteration can be logged which might be useful for ProcessLog operators wrapped
* inside a cross validation. Beside that, all performance estimation operators
* of RapidMiner provide access to the average values calculated during the estimation.
* Since the operator cannot ensure the names of the delivered criteria, the
* ProcessLog operator can access the values via the generic value names:</p>
* <ul>
* <li>performance: the value for the main criterion calculated by this validation operator</li>
* <li>performance1: the value of the first criterion of the performance vector calculated</li>
* <li>performance2: the value of the second criterion of the performance vector calculated</li>
* <li>performance3: the value of the third criterion of the performance vector calculated</li>
* <li>for the main criterion, also the variance and the standard deviation can be
* accessed where applicable.</li>
* </ul>
*
* @rapidminer.index cross-validation
* @author Ingo Mierswa
* @version $Id: BatchXValidation.java,v 1.8 2008/08/25 08:10:35 ingomierswa Exp $
*/
public class BatchXValidation extends ValidationChain {
/** The parameter name for "Indicates if only performance vectors should be averaged or all types of averagable result vectors" */
public static final String PARAMETER_AVERAGE_PERFORMANCES_ONLY = "average_performances_only";
private int iteration;
public BatchXValidation(OperatorDescription description) {
super(description);
addValue(new ValueDouble("iteration", "The number of the current iteration.") {
public double getDoubleValue() {
return iteration;
}
});
}
public IOObject[] estimatePerformance(ExampleSet inputSet) throws OperatorException {
// split by attribute
Attribute batchAttribute = inputSet.getAttributes().getSpecial(Attributes.BATCH_NAME);
if (batchAttribute == null) {
throw new UserError(this, 113, Attributes.BATCH_NAME);
}
SplittedExampleSet splittedES = SplittedExampleSet.splitByAttribute(inputSet, batchAttribute);
// start crossvalidation
List<AverageVector> averageVectors = new ArrayList<AverageVector>();
for (iteration = 0; iteration < splittedES.getNumberOfSubsets(); iteration++) {
splittedES.selectAllSubsetsBut(iteration);
learn(splittedES);
splittedES.selectSingleSubset(iteration);
IOContainer evalOutput = evaluate(splittedES);
Tools.handleAverages(evalOutput, averageVectors, getParameterAsBoolean(PARAMETER_AVERAGE_PERFORMANCES_ONLY));
inApplyLoop();
}
// end crossvalidation
// set last result for plotting purposes. This is an average value and
// actually not the last performance value!
PerformanceVector averagePerformance = Tools.getPerformanceVector(averageVectors);
if (averagePerformance != null)
setResult(averagePerformance);
AverageVector[] result = new AverageVector[averageVectors.size()];
averageVectors.toArray(result);
return result;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeBoolean(PARAMETER_AVERAGE_PERFORMANCES_ONLY, "Indicates if only performance vectors should be averaged or all types of averagable result vectors", true));
return types;
}
}