/*
* 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.awt.Component;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.AbstractModel;
import com.rapidminer.operator.IOContainer;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.Tools;
import Jama.Matrix;
/**
* This is the transformation model of the <code>GHA</code> The number of
* components is initially specified by the <code>GHA</code>. Additionally
* the user can specify a lower number in the <code>ModelApplier</code>. You
* can add two prediction parameter:
* <ul>
* <li><b>number_of_components</b> <i>integer</i> Specify a lower number of
* components
* <li><b>keep_attributes</b> <i>true|false</i> If true, the original
* features are not removed.
* </ul>
*
* @author Daniel Hakenjos, Ingo Mierswa
* @version $Id: GHAModel.java,v 1.5 2008/05/09 19:22:51 ingomierswa Exp $
* @see GHA
*/
public class GHAModel extends AbstractModel implements ComponentWeightsCreatable {
private static final long serialVersionUID = -5204076842779376622L;
private int numberOfAttributes;
private int numberOfComponents;
private double[] means;
private String[] attributeNames;
private List<WeightVector> weightVectors;
private boolean keepAttributes = false;
public GHAModel(ExampleSet eSet, double[] eigenvalues, double[][] weights, double[] mean) {
super(eSet);
keepAttributes = false;
numberOfAttributes = weights[0].length;
numberOfComponents = weights.length;
this.means = mean;
attributeNames = new String[numberOfAttributes];
int i = 0;
for (Attribute attribute : eSet.getAttributes()) {
attributeNames[i++] = attribute.getName();
}
this.weightVectors = new ArrayList<WeightVector>(eigenvalues.length);
for (i = 0; i < eigenvalues.length; i++) {
double[] currentVector = new double[eSet.getAttributes().size()];
for (int j = 0; j < currentVector.length; j++) {
currentVector[j] = weights[j][i];
}
this.weightVectors.add(new WeightVector(currentVector, eigenvalues[i]));
}
Collections.sort(this.weightVectors);
}
public double[] getMean() {
return means;
}
public double[] getWeights(int index) {
return this.weightVectors.get(index).getWeights();
}
public double getEigenvalue(int index) {
return this.weightVectors.get(index).getEigenvalue();
}
public double getNumberOfComponents() {
return numberOfComponents;
}
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
exampleSet.recalculateAllAttributeStatistics();
if (numberOfAttributes != exampleSet.getAttributes().size()) {
throw new UserError(null, 133, numberOfAttributes, exampleSet.getAttributes().size());
}
log("Number of components: " + numberOfComponents);
// 1) prepare data
double[][] data = new double[exampleSet.size()][exampleSet.getAttributes().size()];
boolean haslabel = (exampleSet.getAttributes().getLabel() != null);
boolean haspredlabel = (exampleSet.getAttributes().getPredictedLabel() != null);
double[] labelvalues = new double[0], predvalues = new double[0];
if (haslabel) {
labelvalues = new double[exampleSet.size()];
}
if (haspredlabel) {
predvalues = new double[exampleSet.size()];
}
Iterator<Example> reader = exampleSet.iterator();
Example example;
for (int sample = 0; sample < exampleSet.size(); sample++) {
example = reader.next();
int d = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
data[sample][d] = example.getValue(attribute) - means[d];
if (haslabel) {
labelvalues[sample] = example.getValue(example.getAttributes().getLabel());
}
if (haspredlabel) {
predvalues[sample] = example.getValue(example.getAttributes().getPredictedLabel());
}
d++;
}
}
Matrix dataMatrix = new Matrix(data);
// 2) Derive the new DataSet
double[][] values = new double[this.weightVectors.size()][attributeNames.length];
int counter = 0;
for (WeightVector wv : this.weightVectors) {
values[counter++] = wv.getWeights();
}
Matrix W = new Matrix(values);
Matrix finaldataMatrix = dataMatrix.times(W.transpose());
double[][] finaldata = finaldataMatrix.getArray();
if (!keepAttributes) {
exampleSet.getAttributes().clearRegular();
}
log("Adding new the derived features...");
Attribute[] principalComponentAttributes = new Attribute[numberOfComponents];
for (int i = 0; i < numberOfComponents; i++) {
principalComponentAttributes[i] = AttributeFactory.createAttribute("pc_" + (i + 1), Ontology.REAL);
exampleSet.getExampleTable().addAttribute(principalComponentAttributes[i]);
exampleSet.getAttributes().addRegular(principalComponentAttributes[i]);
}
reader = exampleSet.iterator();
for (int sample = 0; sample < exampleSet.size(); sample++) {
example = reader.next();
for (int d = 0; d < numberOfComponents; d++) {
example.setValue(principalComponentAttributes[d], finaldata[sample][d]);
}
}
return exampleSet;
}
public void setParameter(String name, Object object) throws OperatorException {
if (name.equals("number_of_components")) {
String value = (String) object;
try {
this.numberOfComponents = Math.min(numberOfComponents, Integer.parseInt(value));
} catch (NumberFormatException error) {
super.setParameter(name, value);
}
} else if (name.equals("keep_attributes")) {
String value = (String) object;
keepAttributes = false;
if (value.equals("true")) {
keepAttributes = true;
}
} else {
super.setParameter(name, object);
}
}
public AttributeWeights getWeightsOfComponent(int component) throws OperatorException {
if (component < 1) {
component = 1;
}
if (component > attributeNames.length) {
logWarning("Creating weights of component " + attributeNames.length + "!");
component = attributeNames.length;
}
AttributeWeights weights = new AttributeWeights();
for (int i = 0; i < attributeNames.length; i++) {
weights.setWeight(attributeNames[i], weightVectors.get(component - 1).getWeights()[i]);
}
return weights;
}
public Component getVisualizationComponent(IOContainer container) {
double[] cumulativeVariance = new double[numberOfComponents];
double cumulative = 0.0d;
int i = 0;
for (WeightVector wv : weightVectors) {
cumulative += wv.getEigenvalue();
cumulativeVariance[i++] = cumulative;
}
return (new EigenvectorModelVisualization(getName(), this.attributeNames, cumulativeVariance,
weightVectors, false, numberOfComponents, 1.0d)).getVisualizationComponent(container);
}
public String toString() {
StringBuffer result = new StringBuffer(Tools.getLineSeparator() + "GHA Components:" + Tools.getLineSeparator());
for (int i = 0; i < weightVectors.size(); i++) {
result.append("PC " + (i+1) + ": ");
for (int j = 0; j < attributeNames.length; j++) {
double value = weightVectors.get(j).getWeights()[i];
if (value > 0)
result.append(" + ");
else
result.append(" - ");
result.append(Tools.formatNumber(Math.abs(value)) + " * " + attributeNames[j]);
}
result.append(Tools.getLineSeparator());
}
return result.toString();
}
}