/*
* 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.List;
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.performance.PerformanceVector;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
/**
* This operator evaluates the performance of feature weighting algorithms
* including feature selection. The first inner operator is the weighting
* algorithm to be evaluated itself. It must return an attribute weights vector
* which is applied on the data. Then a new model is created using the second
* inner operator and a performance is retrieved using the third inner operator.
* This performance vector serves as a performance indicator for the actual
* algorithm.
*
* This implementation is described for the {@link RandomSplitValidationChain}.
*
* @author Ingo Mierswa
* @version $Id: RandomSplitWrapperValidationChain.java,v 1.8 2006/04/05
* 08:57:28 ingomierswa Exp $
*/
public class RandomSplitWrapperValidationChain extends WrapperValidationChain {
public static final String PARAMETER_SPLIT_RATIO = "split_ratio";
public static final String PARAMETER_SAMPLING_TYPE = "sampling_type";
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
public RandomSplitWrapperValidationChain(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
double splitRatio = getParameterAsDouble(PARAMETER_SPLIT_RATIO);
SplittedExampleSet eSet = new SplittedExampleSet(getInput(ExampleSet.class), splitRatio, getParameterAsInt(PARAMETER_SAMPLING_TYPE), getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
eSet.selectSingleSubset(0);
AttributeWeights weights = useMethod(eSet).remove(AttributeWeights.class);
SplittedExampleSet newInputSet = (SplittedExampleSet) eSet.clone();
// learn on the same data
learn(new AttributeWeightedExampleSet(newInputSet, weights, 0.0d).createCleanClone());
// testing
newInputSet.selectSingleSubset(1);
IOContainer evalRes = evaluate(new AttributeWeightedExampleSet(newInputSet, weights, 0.0d).createCleanClone());
PerformanceVector pv = evalRes.remove(PerformanceVector.class);
setResult(pv.getMainCriterion());
return new IOObject[] { pv, weights };
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeDouble(PARAMETER_SPLIT_RATIO, "Relative size of the training set", 0, 1, 0.7);
type.setExpert(false);
types.add(type);
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;
}
}