/*
* 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.Iterator;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.AttributeWeightedExampleSet;
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.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeInt;
/**
* This operator evaluates the performance of feature weighting and selection
* algorithms. The first inner operator is the algorithm to be evaluated itself.
* It must return an attribute weights vector which is applied on the test data.
* This fold is used to create a new model using the second inner operator and
* retrieve a performance vector using the third inner operator. This
* performance vector serves as a performance indicator for the actual
* algorithm. This implementation of a MethodValidationChain works similar to
* the {@link XValidation}.
*
* @see com.rapidminer.operator.validation.XValidation
* @author Ingo Mierswa
* @version $Id: WrapperXValidation.java,v 1.7 2006/04/05 08:57:28 ingomierswa
* Exp $
*/
public class WrapperXValidation extends WrapperValidationChain {
/** 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 "Use the given random seed instead of global random numbers (-1: use global)" */
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
/** Total number of iterations. */
private int number;
/** Current iteration. */
private int iteration;
public WrapperXValidation(OperatorDescription description) {
super(description);
addValue(new ValueDouble("iteration", "The number of the current iteration.") {
public double getDoubleValue() {
return iteration;
}
});
}
public IOObject[] apply() throws OperatorException {
ExampleSet eSet = getInput(ExampleSet.class);
if (getParameterAsBoolean(PARAMETER_LEAVE_ONE_OUT)) {
number = eSet.size();
} else {
number = getParameterAsInt(PARAMETER_NUMBER_OF_VALIDATIONS);
}
int samplingType = getParameterAsInt(PARAMETER_SAMPLING_TYPE);
int randomSeed = getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED);
SplittedExampleSet inputSet = new SplittedExampleSet(eSet, number, samplingType, randomSeed);
log("Starting " + number + "-fold method cross validation");
// statistics init
PerformanceVector performanceVector = null;
AttributeWeights globalWeights = new AttributeWeights();
for (Attribute attribute : eSet.getAttributes()) {
globalWeights.setWeight(attribute.getName(), 0.0d);
}
for (iteration = 0; iteration < number; iteration++) {
// training
inputSet.selectAllSubsetsBut(iteration);
// apply method
AttributeWeights weights = useMethod(inputSet).remove(AttributeWeights.class);
SplittedExampleSet newInputSet = (SplittedExampleSet) inputSet.clone();
// learn on the same data
learn(new AttributeWeightedExampleSet(newInputSet, weights, 0.0d).createCleanClone());
// testing
newInputSet.selectSingleSubset(iteration);
IOContainer evalOutput = evaluate(new AttributeWeightedExampleSet(newInputSet, weights, 0.0d).createCleanClone());
// retrieve performance
PerformanceVector iterationPerformance = evalOutput.remove(PerformanceVector.class);
// build performance average
if (performanceVector == null) {
performanceVector = iterationPerformance;
} else {
for (int i = 0; i < performanceVector.size(); i++) {
performanceVector.getCriterion(i).buildAverage(iterationPerformance.getCriterion(i));
}
}
// build weights average
handleWeights(globalWeights, weights);
setResult(iterationPerformance.getMainCriterion());
inApplyLoop();
}
// end of cross validation
// build average of weights
Iterator i = globalWeights.getAttributeNames().iterator();
while (i.hasNext()) {
String currentName = (String) i.next();
globalWeights.setWeight(currentName, globalWeights.getWeight(currentName) / number);
}
setResult(performanceVector.getMainCriterion());
return new IOObject[] { performanceVector, globalWeights };
}
private void handleWeights(AttributeWeights globalWeights, AttributeWeights currentWeights) {
Iterator i = currentWeights.getAttributeNames().iterator();
while (i.hasNext()) {
String currentName = (String) i.next();
double globalWeight = globalWeights.getWeight(currentName);
double currentWeight = currentWeights.getWeight(currentName);
if (Double.isNaN(globalWeight)) {
globalWeights.setWeight(currentName, currentWeight);
} else {
globalWeights.setWeight(currentName, globalWeight + currentWeight);
}
}
}
public Class<?>[] getOutputClasses() {
return new Class[] { PerformanceVector.class, AttributeWeights.class };
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_OF_VALIDATIONS, "Number of subsets for the crossvalidation", 2, Integer.MAX_VALUE, 10);
type.setExpert(false);
types.add(type);
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));
types.add(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));
types.add(new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global)", -1, Integer.MAX_VALUE, -1));
return types;
}
}