/*
* 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.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.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.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
import com.rapidminer.parameter.conditions.EqualTypeCondition;
import com.rapidminer.tools.math.AverageVector;
/**
* <p>
* <code>XValidation</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>
* 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>
* Like other validation schemes the RapidMiner cross validation can use several types
* of sampling for building the subsets. Linear sampling simply divides the
* example set into partitions without changing the order of the examples.
* Shuffled sampling build random subsets from the data. Stratifed sampling
* builds random subsets and ensures that the class distribution in the subsets
* is the same as in the whole example set.
* </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: XValidation.java,v 1.11 2008/08/25 08:10:35 ingomierswa Exp $
*/
public class XValidation extends ValidationChain {
/** The parameter name for "Number of subsets for the crossvalidation." */
public static final String PARAMETER_NUMBER_OF_VALIDATIONS = "number_of_validations";
/** The parameter name for "Set the number of validations to the number of examples. If set to true, number_of_validations is ignored" */
public static final String PARAMETER_LEAVE_ONE_OUT = "leave_one_out";
/** The parameter name for "Defines the sampling type of the cross validation (linear = consecutive subsets, shuffled = random subsets, stratified = random subsets with class distribution kept constant)" */
public static final String PARAMETER_SAMPLING_TYPE = "sampling_type";
/** 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";
/** The parameter name for "Use the given random seed instead of global random numbers (-1: use global)" */
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
private int iteration;
public XValidation(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 {
int number;
if (getParameterAsBoolean(PARAMETER_LEAVE_ONE_OUT)) {
number = inputSet.size();
} else {
number = getParameterAsInt(PARAMETER_NUMBER_OF_VALIDATIONS);
}
log("Starting " + number + "-fold cross validation");
// Split training / test set
int samplingType = getParameterAsInt(PARAMETER_SAMPLING_TYPE);
int randomSeed = getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED);
SplittedExampleSet splittedES = new SplittedExampleSet(inputSet, number, samplingType, randomSeed);
// start crossvalidation
List<AverageVector> averageVectors = new ArrayList<AverageVector>();
for (iteration = 0; iteration < number; 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));
types.add(new ParameterTypeBoolean(PARAMETER_LEAVE_ONE_OUT, "Set the number of validations to the number of examples. If set to true, number_of_validations is ignored", false));
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_OF_VALIDATIONS, "Number of subsets for the crossvalidation.", 2, Integer.MAX_VALUE, 10);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_LEAVE_ONE_OUT, false));
type.setExpert(false);
types.add(type);
type = new ParameterTypeCategory(PARAMETER_SAMPLING_TYPE, "Defines the sampling type of the cross validation (linear = consecutive subsets, shuffled = random subsets, stratified = random subsets with class distribution kept constant)", SplittedExampleSet.SAMPLING_NAMES, SplittedExampleSet.STRATIFIED_SAMPLING);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_LEAVE_ONE_OUT, false));
types.add(type);
type = new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global)", -1, Integer.MAX_VALUE, -1);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_LEAVE_ONE_OUT, false));
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_SAMPLING_TYPE, false, SplittedExampleSet.SHUFFLED_SAMPLING, SplittedExampleSet.STRATIFIED_SAMPLING));
types.add(type);
return types;
}
}