/*
* RapidMiner
*
* Copyright (C) 2001-2011 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.
*
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* along with this program. If not, see http://www.gnu.org/licenses/.
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package com.rapidminer.operator.features.transformation;
import java.util.Arrays;
import Jama.Matrix;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.Attributes;
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.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.Tools;
/**
* This is the transformation model of the principal components analysis. The
* number of components is initially specified by the <code>PCA</code>.
* Additionally you can specify the number of components in the <code>ModelApplier</code>. You can add two prediction parameter:
* <ul>
* <li><b>variance_threshold</b> <i>double</i> Specify a new threshold for the cumulative variance of the principal components.
* <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 Sebastian Land, Daniel Hakenjos, Ingo Mierswa
* @see PCA
*/
public class SVDModel extends AbstractModel implements ComponentWeightsCreatable {
private static final long serialVersionUID = 5424591594470376525L;
private Matrix vMatrix;
private double[] singularValues;
private double[] cumulativeSingularValueProportion;
private double singularValuesSum;
private String[] attributeNames;
private boolean manualNumber;
private int numberOfComponents = -1;
private double proportionThreshold;
private boolean useLegacyNames = false;
// -----------------------------------
private boolean keepAttributes = false;
public SVDModel(ExampleSet exampleSet, double[] singularValues, Matrix vMatrix) {
super(exampleSet);
this.vMatrix = vMatrix;
this.singularValues = singularValues;
this.keepAttributes = false;
this.attributeNames = new String[exampleSet.getAttributes().size()];
int counter = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
attributeNames[counter] = attribute.getName();
counter++;
}
// compute cumulative values
cumulativeSingularValueProportion = new double[singularValues.length];
// insert cumulative sum of singular values
singularValuesSum = 0.0d;
for (int i = 0; i < singularValues.length; i++) {
singularValuesSum += singularValues[i];
cumulativeSingularValueProportion[i] = singularValuesSum;
}
// now reduce to proportion
for (int i = 0; i < singularValues.length; i++) {
cumulativeSingularValueProportion[i] /= singularValuesSum;
}
}
public String[] getAttributeNames() {
return attributeNames;
}
public double getSingularValue(int index) {
return this.singularValues[index];
}
public double getSingularValueProportion(int index) {
return this.singularValues[index] / singularValuesSum;
}
public double getCumulativeSingularValue(int index) {
return this.cumulativeSingularValueProportion[index] * singularValuesSum;
}
public double getCumulativeSingularValueProportion(int index) {
return this.cumulativeSingularValueProportion[index];
}
public double getSingularVectorValue(int vectorIndex, int component) {
return this.vMatrix.get(component, vectorIndex);
}
public double getProportionThreshold() {
return this.proportionThreshold;
}
public int getMaximumNumberOfComponents() {
return attributeNames.length;
}
/**
* This returns the total number of possible components.
*/
public int getNumberOfComponents() {
return singularValues.length;
}
public void setVarianceThreshold(double threshold) {
this.manualNumber = false;
this.proportionThreshold = threshold;
this.numberOfComponents = -1;
}
public void setNumberOfComponents(int numberOfComponents) {
this.proportionThreshold = 0.95;
this.manualNumber = true;
this.numberOfComponents = numberOfComponents;
}
@Override
public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {
Attributes attributes = exampleSet.getAttributes();
if (attributeNames.length != attributes.size()) {
throw new UserError(null, 133, numberOfComponents, attributes.size());
}
// remember attributes that have been removed during training. These will be removed lateron
Attribute[] inputAttributes = new Attribute[getTrainingHeader().getAttributes().size()];
int d = 0;
for (Attribute oldAttribute : getTrainingHeader().getAttributes()) {
inputAttributes[d] = attributes.get(oldAttribute.getName());
d++;
}
// determining number of used components
int numberOfUsedComponents = -1;
if (manualNumber) {
numberOfUsedComponents = numberOfComponents;
} else {
if (proportionThreshold == 0.0d) {
numberOfUsedComponents = -1;
} else {
numberOfUsedComponents = 0;
while (cumulativeSingularValueProportion[numberOfUsedComponents] < proportionThreshold) {
numberOfUsedComponents++;
}
numberOfUsedComponents++;
}
}
// if nothing defined or number exceeds maximal number of possible components
if (numberOfUsedComponents == -1 || numberOfUsedComponents > getNumberOfComponents()) {
// keep all components
numberOfUsedComponents = getNumberOfComponents();
}
// retrieve factors inside singularValueVectors
double[][] singularValueFactors = new double[numberOfUsedComponents][attributeNames.length];
double[][] vMatrixData = vMatrix.getArray();
for (int i = 0; i < numberOfUsedComponents; i++) {
double invertedSingularValue = 1d / singularValues[i];
for (int j = 0; j < attributeNames.length; j++) {
singularValueFactors[i][j] = vMatrixData[j][i] * invertedSingularValue;
}
}
// now build new attributes
Attribute[] derivedAttributes = new Attribute[numberOfUsedComponents];
for (int i = 0; i < numberOfUsedComponents; i++) {
if (useLegacyNames)
derivedAttributes[i] = AttributeFactory.createAttribute("d" + (i), Ontology.REAL);
else
derivedAttributes[i] = AttributeFactory.createAttribute("svd_" + (i + 1), Ontology.REAL);
exampleSet.getExampleTable().addAttribute(derivedAttributes[i]);
attributes.addRegular(derivedAttributes[i]);
}
// now iterator through all examples and derive value of new features
double[] derivedValues = new double[numberOfUsedComponents];
for (Example example : exampleSet) {
// calculate values of new attributes with single scan over attributes
d = 0;
for (Attribute attribute : inputAttributes) {
double attributeValue = example.getValue(attribute);
for (int i = 0; i < numberOfUsedComponents; i++) {
derivedValues[i] += singularValueFactors[i][d] * attributeValue;
}
d++;
}
// set values
for (int i = 0; i < numberOfUsedComponents; i++) {
example.setValue(derivedAttributes[i], derivedValues[i]);
}
// set values back
Arrays.fill(derivedValues, 0);
}
// now remove attributes if needed
if (!keepAttributes) {
for (Attribute attribute : inputAttributes) {
attributes.remove(attribute);
}
}
return exampleSet;
}
public void enableLegacyMode() {
this.useLegacyNames = true;
}
@Override
public void setParameter(String name, Object object) throws OperatorException {
if (name.equals("proportion_threshold")) {
String value = (String) object;
try {
this.setVarianceThreshold(Double.parseDouble(value));
} catch (NumberFormatException error) {
super.setParameter(name, value);
}
} else if (name.equals("number_of_components")) {
String value = (String) object;
try {
this.setNumberOfComponents(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();
double[] singularVector = vMatrix.getArray()[component];
for (int i = 0; i < attributeNames.length; i++) {
weights.setWeight(attributeNames[i], singularVector[i]);
}
return weights;
}
@Override
public String toResultString() {
StringBuilder result = new StringBuilder(Tools.getLineSeparator() + "Principal Components:" + Tools.getLineSeparator());
if (manualNumber) {
result.append("Number of Components: " + numberOfComponents + Tools.getLineSeparator());
} else {
result.append("Proportion Threshold: " + proportionThreshold + Tools.getLineSeparator());
}
for (int i = 0; i < vMatrix.getColumnDimension(); i++) {
result.append("PC " + (i + 1) + ": ");
for (int j = 0; j < attributeNames.length; j++) {
double value = vMatrix.get(i,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 String toString() {
StringBuilder result = new StringBuilder(Tools.getLineSeparator() + "Principal Components:" + Tools.getLineSeparator());
if (manualNumber) {
result.append("Number of Components: " + numberOfComponents + Tools.getLineSeparator());
} else {
result.append("Variance Threshold: " + proportionThreshold + Tools.getLineSeparator());
}
return result.toString();
}
}