/**
* 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.transformation;
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.gui.renderer.models.EigenvectorModelEigenvalueRenderer.EigenvalueTableModel;
import com.rapidminer.gui.renderer.models.EigenvectorModelEigenvectorRenderer.EigenvectorTableModel;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.OperatorProgress;
import com.rapidminer.operator.UserError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.Tools;
import com.rapidminer.tools.math.VectorMath;
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
* @see GHA
*/
public class GHAModel extends AbstractEigenvectorModel implements ComponentWeightsCreatable {
private static final long serialVersionUID = -5204076842779376622L;
/**
* The progress of this operator is split into 5 part-progresses. These values define how many
* percent are completed after part-progress 1, part-progress 2, etc.
*/
private static final int INTERMEDIATE_PROGRESS_1 = 20;
private static final int INTERMEDIATE_PROGRESS_2 = 40;
private static final int INTERMEDIATE_PROGRESS_3 = 60;
private static final int INTERMEDIATE_PROGRESS_4 = 80;
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[i][j];
}
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;
}
@Override
public ExampleSet apply(ExampleSet inputExampleSet) throws OperatorException {
ExampleSet exampleSet = (ExampleSet) inputExampleSet.clone();
exampleSet.recalculateAllAttributeStatistics();
if (numberOfAttributes != exampleSet.getAttributes().size()) {
throw new UserError(null, 133, numberOfAttributes, exampleSet.getAttributes().size());
}
// 1) prepare data
double[][] data = new double[exampleSet.size()][exampleSet.getAttributes().size()];
OperatorProgress progress = null;
if (getShowProgress() && getOperator() != null && getOperator().getProgress() != null) {
progress = getOperator().getProgress();
progress.setTotal(100);
}
int index = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
Iterator<Example> reader = exampleSet.iterator();
for (int sample = 0; sample < exampleSet.size(); sample++) {
Example example = reader.next();
data[sample][index] = example.getValue(attribute) - means[index];
}
index++;
if (progress != null) {
progress.setCompleted((int) ((double) INTERMEDIATE_PROGRESS_1 * index / exampleSet.getAttributes().size()));
}
}
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();
if (progress != null) {
progress.setCompleted(
(int) (((double) INTERMEDIATE_PROGRESS_2 - INTERMEDIATE_PROGRESS_1) * counter / weightVectors.size()
+ INTERMEDIATE_PROGRESS_1));
}
}
Matrix W = new Matrix(values);
Matrix finaldataMatrix = dataMatrix.times(W.transpose());
double[][] finaldata = finaldataMatrix.getArray();
if (!keepAttributes) {
exampleSet.getAttributes().clearRegular();
}
if (progress != null) {
progress.setCompleted(INTERMEDIATE_PROGRESS_3);
}
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]);
if (progress != null) {
progress.setCompleted(
(int) (((double) INTERMEDIATE_PROGRESS_4 - INTERMEDIATE_PROGRESS_3) * i / numberOfComponents
+ INTERMEDIATE_PROGRESS_3));
}
}
for (int d = 0; d < numberOfComponents; d++) {
Iterator<Example> reader = exampleSet.iterator();
for (int sample = 0; sample < exampleSet.size(); sample++) {
Example example = reader.next();
example.setValue(principalComponentAttributes[d], finaldata[sample][d]);
}
if (progress != null) {
progress.setCompleted(
(int) ((100.0 - INTERMEDIATE_PROGRESS_4) * d / numberOfComponents + INTERMEDIATE_PROGRESS_4));
}
}
return exampleSet;
}
@Override
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);
}
}
@Override
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;
}
@Override
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(i).getWeights()[j];
if (value > 0) {
result.append(" + ");
} else {
result.append(" - ");
}
result.append(Tools.formatNumber(Math.abs(value)) + " * " + attributeNames[j]);
}
result.append(Tools.getLineSeparator());
}
return result.toString();
}
@Override
public EigenvectorTableModel getEigenvectorTableModel() {
return new EigenvectorTableModel(weightVectors, attributeNames, attributeNames.length);
}
@Override
public EigenvalueTableModel getEigenvalueTableModel() {
double[] cumulativeVariance = new double[numberOfComponents];
double varianceSum = 0.0d;
int i = 0;
for (WeightVector wv : weightVectors) {
varianceSum += wv.getEigenvalue();
cumulativeVariance[i++] = varianceSum;
}
return new EigenvalueTableModel(weightVectors, VectorMath.vectorDivision(cumulativeVariance, varianceSum),
varianceSum);
}
@Override
public double[] getCumulativeVariance() {
double[] cumulativeVariance = new double[numberOfComponents];
double varianceSum = 0.0d;
int i = 0;
for (WeightVector wv : weightVectors) {
varianceSum += wv.getEigenvalue();
cumulativeVariance[i++] = varianceSum;
}
return VectorMath.vectorDivision(cumulativeVariance, varianceSum);
}
}