/**
* 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.features.weighting;
import java.util.List;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorCreationException;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.features.transformation.PCA;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.OperatorService;
/**
* Uses the factors of one of the principal components (default is the first) as feature weights.
* Please note that the PCA weighting operator is currently the only one which also works on data
* sets without a label, i.e. for unsupervised learning.
*
* @author Ingo Mierswa
*
*/
public class PCAWeighting extends AbstractWeighting {
public PCAWeighting(OperatorDescription description) {
super(description, false);
}
@Override
protected AttributeWeights calculateWeights(ExampleSet exampleSet) throws OperatorException {
PCA pcaOperator = null;
try {
pcaOperator = OperatorService.createOperator(PCA.class);
} catch (OperatorCreationException e) {
throw new UserError(this, 904, "inner pca operator", e.getMessage());
}
pcaOperator.setParameter(PCA.PARAMETER_REDUCTION_TYPE, PCA.REDUCTION_NONE + "");
ComponentWeights weightOperator = null;
try {
weightOperator = OperatorService.createOperator(ComponentWeights.class);
} catch (OperatorCreationException e) {
throw new UserError(this, 904, "inner weight operator", e.getMessage());
}
weightOperator.setParameter(ComponentWeights.PARAMETER_COMPONENT_NUMBER,
getParameterAsInt(ComponentWeights.PARAMETER_COMPONENT_NUMBER) + "");
weightOperator.setParameter(AbstractWeighting.PARAMETER_NORMALIZE_WEIGHTS, false + "");
weightOperator.setParameter(AbstractWeighting.PARAMETER_SORT_WEIGHTS, false + "");
Model pcaModel = pcaOperator.doWork(exampleSet);
AttributeWeights result = weightOperator.doWork(pcaModel, exampleSet);
result.setSource(this.getName());
return result;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeInt(ComponentWeights.PARAMETER_COMPONENT_NUMBER,
"Indicates the number of the component from which the weights should be calculated.", 1, Integer.MAX_VALUE,
1));
return types;
}
@Override
public boolean supportsCapability(OperatorCapability capability) {
switch (capability) {
case NUMERICAL_ATTRIBUTES:
case NO_LABEL:
return true;
default:
return false;
}
}
}