/*
* 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.features.transformation;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.tools.WekaInstancesAdaptor;
import com.rapidminer.tools.WekaTools;
import weka.attributeSelection.PrincipalComponents;
import weka.core.Instances;
/**
* Builds the principal components of the given data. The user can specify the
* amount of variance to cover in the original data when retaining the best
* number of principal components. This operator makes use of the Weka
* implementation <code>PrincipalComponent</code>.
*
* @author Ingo Mierswa
* @version $Id: PrincipalComponentsTransformation.java,v 1.1 2006/04/14
* 13:07:13 ingomierswa Exp $
*/
public class PrincipalComponentsTransformation extends Operator {
/** The parameter name for "The minimum variance to cover in the original data to determine the number of principal components." */
public static final String PARAMETER_MIN_VARIANCE_COVERAGE = "min_variance_coverage";
public PrincipalComponentsTransformation(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
ExampleSet exampleSet = getInput(ExampleSet.class);
PrincipalComponents transformation = new PrincipalComponents();
transformation.setNormalize(false); // if the user wants to normalize
// the data he has to apply the
// filter before
transformation.setVarianceCovered(getParameterAsDouble(PARAMETER_MIN_VARIANCE_COVERAGE));
log(getName() + ": Converting to Weka instances.");
Instances instances = WekaTools.toWekaInstances(exampleSet, "PCAInstances", WekaInstancesAdaptor.LEARNING);
try {
log(getName() + ": Building principal components.");
transformation.buildEvaluator(instances);
} catch (Exception e) {
throw new UserError(this, e, 905, new Object[] { "PrincipalComponents", e });
}
ExampleSet result = null;
try {
Instances transformed = transformation.transformedData();
result = WekaTools.toRapidMinerExampleSet(transformed, "pc");
} catch (Exception e) {
throw new UserError(this, 905, "Principal Components Transformation", "Cannot convert to principal components (" + e.getMessage() + ")");
}
return new IOObject[] { result };
}
public Class<?>[] getOutputClasses() {
return new Class[] { ExampleSet.class };
}
public Class<?>[] getInputClasses() {
return new Class[] { ExampleSet.class };
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeDouble(PARAMETER_MIN_VARIANCE_COVERAGE, "The minimum variance to cover in the original data to determine the number of principal components.", 0.0, 1.0, 0.95);
type.setExpert(false);
types.add(type);
return types;
}
}