/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/*
* RandomProjection.java
* Copyright (C) 2003-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.attribute;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.DenseInstance;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
/**
<!-- globalinfo-start -->
* Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (i.e. It will reduce the number of attributes in the data while preserving much of its variation like PCA, but at a much less computational cost).<br/>
* It first applies the NominalToBinary filter to convert all attributes to numeric before reducing the dimension. It preserves the class attribute.<br/>
* <br/>
* For more information, see:<br/>
* <br/>
* Dmitriy Fradkin, David Madigan: Experiments with random projections for machine learning. In: KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 517-522, 003.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Fradkin003,
* address = {New York, NY, USA},
* author = {Dmitriy Fradkin and David Madigan},
* booktitle = {KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining},
* pages = {517-522},
* publisher = {ACM Press},
* title = {Experiments with random projections for machine learning},
* year = {003}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N <number>
* The number of dimensions (attributes) the data should be reduced to
* (default 10; exclusive of the class attribute, if it is set).</pre>
*
* <pre> -D [SPARSE1|SPARSE2|GAUSSIAN]
* The distribution to use for calculating the random matrix.
* Sparse1 is:
* sqrt(3)*{-1 with prob(1/6), 0 with prob(2/3), +1 with prob(1/6)}
* Sparse2 is:
* {-1 with prob(1/2), +1 with prob(1/2)}
* </pre>
*
* <pre> -P <percent>
* The percentage of dimensions (attributes) the data should
* be reduced to (exclusive of the class attribute, if it is set). This -N
* option is ignored if this option is present or is greater
* than zero.</pre>
*
* <pre> -M
* Replace missing values using the ReplaceMissingValues filter</pre>
*
* <pre> -R <num>
* The random seed for the random number generator used for
* calculating the random matrix (default 42).</pre>
*
<!-- options-end -->
*
* @author Ashraf M. Kibriya (amk14@cs.waikato.ac.nz)
* @version $Revision: 8034 $ [1.0 - 22 July 2003 - Initial version (Ashraf M. Kibriya)]
*/
public class RandomProjection
extends Filter
implements UnsupervisedFilter, OptionHandler, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 4428905532728645880L;
/** Stores the number of dimensions to reduce the data to */
protected int m_k = 10;
/** Stores the dimensionality the data should be reduced to as percentage of the original dimension */
protected double m_percent = 0.0;
/** Is the random matrix will be computed using
Gaussian distribution or not */
protected boolean m_useGaussian = false;
/** distribution type: sparse 1 */
public static final int SPARSE1 = 1;
/** distribution type: sparse 2 */
public static final int SPARSE2 = 2;
/** distribution type: gaussian */
public static final int GAUSSIAN = 3;
/** The types of distributions that can be used for
calculating the random matrix */
public static final Tag [] TAGS_DSTRS_TYPE = {
new Tag(SPARSE1, "Sparse1"),
new Tag(SPARSE2, "Sparse2"),
new Tag(GAUSSIAN, "Gaussian"),
};
/** Stores the distribution to use for calculating the
random matrix */
protected int m_distribution = SPARSE1;
/** Should the missing values be replaced using
unsupervised.ReplaceMissingValues filter */
protected boolean m_useReplaceMissing = false;
/** Keeps track of output format if it is defined or not */
protected boolean m_OutputFormatDefined = false;
/** The NominalToBinary filter applied to the data before this filter */
protected Filter m_ntob; // = new weka.filters.unsupervised.attribute.NominalToBinary();
/** The ReplaceMissingValues filter */
protected Filter m_replaceMissing;
/** Stores the random seed used to generate the random matrix */
protected long m_rndmSeed = 42;
/** The random matrix */
protected double m_rmatrix[][];
/** The random number generator used for generating the random matrix */
protected Random m_random;
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(2);
newVector.addElement(new Option(
"\tThe number of dimensions (attributes) the data should be reduced to\n"
+"\t(default 10; exclusive of the class attribute, if it is set).",
"N", 1, "-N <number>"));
newVector.addElement(new Option(
"\tThe distribution to use for calculating the random matrix.\n"
+"\tSparse1 is:\n"
+"\t sqrt(3)*{-1 with prob(1/6), 0 with prob(2/3), +1 with prob(1/6)}\n"
+"\tSparse2 is:\n"
+"\t {-1 with prob(1/2), +1 with prob(1/2)}\n",
"D", 1, "-D [SPARSE1|SPARSE2|GAUSSIAN]"));
//newVector.addElement(new Option(
// "\tUse Gaussian distribution for calculating the random matrix.",
// "G", 0, "-G"));
newVector.addElement(new Option(
"\tThe percentage of dimensions (attributes) the data should\n"
+"\tbe reduced to (exclusive of the class attribute, if it is set). This -N\n"
+"\toption is ignored if this option is present or is greater\n"
+"\tthan zero.",
"P", 1, "-P <percent>"));
newVector.addElement(new Option(
"\tReplace missing values using the ReplaceMissingValues filter",
"M", 0, "-M"));
newVector.addElement(new Option(
"\tThe random seed for the random number generator used for\n"
+"\tcalculating the random matrix (default 42).",
"R", 0, "-R <num>"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N <number>
* The number of dimensions (attributes) the data should be reduced to
* (default 10; exclusive of the class attribute, if it is set).</pre>
*
* <pre> -D [SPARSE1|SPARSE2|GAUSSIAN]
* The distribution to use for calculating the random matrix.
* Sparse1 is:
* sqrt(3)*{-1 with prob(1/6), 0 with prob(2/3), +1 with prob(1/6)}
* Sparse2 is:
* {-1 with prob(1/2), +1 with prob(1/2)}
* </pre>
*
* <pre> -P <percent>
* The percentage of dimensions (attributes) the data should
* be reduced to (exclusive of the class attribute, if it is set). This -N
* option is ignored if this option is present or is greater
* than zero.</pre>
*
* <pre> -M
* Replace missing values using the ReplaceMissingValues filter</pre>
*
* <pre> -R <num>
* The random seed for the random number generator used for
* calculating the random matrix (default 42).</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String mString = Utils.getOption('P', options);
if (mString.length() != 0) {
setPercent((double) Double.parseDouble(mString)); //setNumberOfAttributes((int) Integer.parseInt(mString));
} else {
setPercent(0);
mString = Utils.getOption('N', options);
if (mString.length() != 0)
setNumberOfAttributes(Integer.parseInt(mString));
else
setNumberOfAttributes(10);
}
mString = Utils.getOption('R', options);
if(mString.length()!=0) {
setRandomSeed( Long.parseLong(mString) );
}
mString = Utils.getOption('D', options);
if(mString.length()!=0) {
if(mString.equalsIgnoreCase("sparse1"))
setDistribution( new SelectedTag(SPARSE1, TAGS_DSTRS_TYPE) );
else if(mString.equalsIgnoreCase("sparse2"))
setDistribution( new SelectedTag(SPARSE2, TAGS_DSTRS_TYPE) );
else if(mString.equalsIgnoreCase("gaussian"))
setDistribution( new SelectedTag(GAUSSIAN, TAGS_DSTRS_TYPE) );
}
if(Utils.getFlag('M', options))
setReplaceMissingValues(true);
else
setReplaceMissingValues(false);
//if(Utils.getFlag('G', options))
// setUseGaussian(true);
//else
// setUseGaussian(false);
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] options = new String [10];
int current = 0;
//if (getUseGaussian()) {
// options[current++] = "-G";
//}
if (getReplaceMissingValues()) {
options[current++] = "-M";
}
if (getPercent() == 0) {
options[current++] = "-N";
options[current++] = "" + getNumberOfAttributes();
}
else {
options[current++] = "-P";
options[current++] = "" + getPercent();
}
options[current++] = "-R";
options[current++] = "" + getRandomSeed();
SelectedTag t = getDistribution();
options[current++] = "-D";
options[current++] = ""+t.getSelectedTag().getReadable();
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Reduces the dimensionality of the data by projecting"
+ " it onto a lower dimensional subspace using a random"
+ " matrix with columns of unit length (i.e. It will reduce"
+ " the number of attributes in the data while preserving"
+ " much of its variation like PCA, but at a much less"
+ " computational cost).\n"
+ "It first applies the NominalToBinary filter to"
+ " convert all attributes to numeric before reducing the"
+ " dimension. It preserves the class attribute.\n\n"
+ "For more information, see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Dmitriy Fradkin and David Madigan");
result.setValue(Field.TITLE, "Experiments with random projections for machine learning");
result.setValue(Field.BOOKTITLE, "KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining");
result.setValue(Field.YEAR, "003");
result.setValue(Field.PAGES, "517-522");
result.setValue(Field.PUBLISHER, "ACM Press");
result.setValue(Field.ADDRESS, "New York, NY, USA");
return result;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numberOfAttributesTipText() {
return "The number of dimensions (attributes) the data should"
+ " be reduced to.";
}
/**
* Sets the number of attributes (dimensions) the data should be reduced to
*
* @param newAttNum the goal for the dimensions
*/
public void setNumberOfAttributes(int newAttNum) {
m_k = newAttNum;
}
/**
* Gets the current number of attributes (dimensionality) to which the data
* will be reduced to.
*
* @return the number of dimensions
*/
public int getNumberOfAttributes() {
return m_k;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String percentTipText() {
return " The percentage of dimensions (attributes) the data should"
+ " be reduced to (inclusive of the class attribute). This "
+ " NumberOfAttributes option is ignored if this option is"
+ " present or is greater than zero.";
}
/**
* Sets the percent the attributes (dimensions) of the data should be reduced to
*
* @param newPercent the percentage of attributes
*/
public void setPercent(double newPercent) {
if(newPercent > 0)
newPercent /= 100;
m_percent = newPercent;
}
/**
* Gets the percent the attributes (dimensions) of the data will be reduced to
*
* @return the percentage of attributes
*/
public double getPercent() {
return m_percent * 100;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String randomSeedTipText() {
return "The random seed used by the random"
+" number generator used for generating"
+" the random matrix ";
}
/**
* Sets the random seed of the random number generator
*
* @param seed the random seed value
*/
public void setRandomSeed(long seed) {
m_rndmSeed = seed;
}
/**
* Gets the random seed of the random number generator
*
* @return the random seed value
*/
public long getRandomSeed() {
return m_rndmSeed;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String distributionTipText() {
return "The distribution to use for calculating the random matrix.\n"
+"Sparse1 is:\n"
+" sqrt(3) * { -1 with prob(1/6), \n"
+" 0 with prob(2/3), \n"
+" +1 with prob(1/6) } \n"
+"Sparse2 is:\n"
+" { -1 with prob(1/2), \n"
+" +1 with prob(1/2) } ";
}
/**
* Sets the distribution to use for calculating the random matrix
*
* @param newDstr the distribution to use
*/
public void setDistribution(SelectedTag newDstr) {
if (newDstr.getTags() == TAGS_DSTRS_TYPE) {
m_distribution = newDstr.getSelectedTag().getID();
}
}
/**
* Returns the current distribution that'll be used for calculating the
* random matrix
*
* @return the current distribution
*/
public SelectedTag getDistribution() {
return new SelectedTag(m_distribution, TAGS_DSTRS_TYPE);
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String replaceMissingValuesTipText() {
return "If set the filter uses weka.filters.unsupervised.attribute.ReplaceMissingValues"
+ " to replace the missing values";
}
/**
* Sets either to use replace missing values filter or not
*
* @param t if true then the replace missing values is used
*/
public void setReplaceMissingValues(boolean t) {
m_useReplaceMissing = t;
}
/**
* Gets the current setting for using ReplaceMissingValues filter
*
* @return true if the replace missing values filter is used
*/
public boolean getReplaceMissingValues() {
return m_useReplaceMissing;
}
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enableAllAttributes();
result.enable(Capability.MISSING_VALUES);
// class
result.enableAllClasses();
result.enable(Capability.MISSING_CLASS_VALUES);
result.enable(Capability.NO_CLASS);
return result;
}
/**
* Sets the format of the input instances.
*
* @param instanceInfo an Instances object containing the input
* instance structure (any instances contained in the object are
* ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @throws Exception if the input format can't be set
* successfully
*/
public boolean setInputFormat(Instances instanceInfo) throws Exception {
super.setInputFormat(instanceInfo);
/*
if (instanceInfo.classIndex() < 0) {
throw new UnassignedClassException("No class has been assigned to the instances");
}
*/
for(int i=0; i<instanceInfo.numAttributes(); i++) {
if( i!=instanceInfo.classIndex() && instanceInfo.attribute(i).isNominal() ) {
if(instanceInfo.classIndex()>=0)
m_ntob = new weka.filters.supervised.attribute.NominalToBinary();
else
m_ntob = new weka.filters.unsupervised.attribute.NominalToBinary();
break;
}
}
//r.setSeed(m_rndmSeed); //in case the setRandomSeed() is not
//called we better set the seed to its
//default value of 42.
boolean temp=true;
if(m_replaceMissing!=null) {
m_replaceMissing = new weka.filters.unsupervised.attribute.ReplaceMissingValues();
if(m_replaceMissing.setInputFormat(instanceInfo))
temp=true;
else
temp=false;
}
if(m_ntob!=null) {
if(m_ntob.setInputFormat(instanceInfo)) {
setOutputFormat();
return temp && true;
}
else {
return false;
}
}
else {
setOutputFormat();
return temp && true;
}
}
/**
* Input an instance for filtering.
*
* @param instance the input instance
* @return true if the filtered instance may now be
* collected with output().
* @throws IllegalStateException if no input format has been set
*/
public boolean input(Instance instance) throws Exception {
Instance newInstance=null;
if (getInputFormat()==null) {
throw new IllegalStateException("No input instance format defined");
}
if(m_NewBatch) {
resetQueue();
//if(ntob!=null)
// ntob.m_NewBatch=true;
m_NewBatch = false;
}
boolean replaceDone=false;
if(m_replaceMissing!=null) {
if(m_replaceMissing.input(instance)) {
if(m_OutputFormatDefined == false)
setOutputFormat();
newInstance = m_replaceMissing.output();
replaceDone = true;
}
else
return false;;
}
if(m_ntob!=null) {
if(replaceDone==false)
newInstance = instance;
if(m_ntob.input(newInstance)) {
if(m_OutputFormatDefined == false)
setOutputFormat();
newInstance = m_ntob.output();
newInstance = convertInstance(newInstance);
push(newInstance);
return true;
}
else {
return false;
}
}
else {
if(replaceDone==false)
newInstance = instance;
newInstance = convertInstance(newInstance);
push(newInstance);
return true;
}
}
/**
* Signify that this batch of input to the filter is finished.
*
* @return true if there are instances pending output
* @throws NullPointerException if no input structure has been defined,
* @throws Exception if there was a problem finishing the batch.
*/
public boolean batchFinished() throws Exception {
if (getInputFormat() == null) {
throw new NullPointerException("No input instance format defined");
}
boolean conversionDone=false;
if(m_replaceMissing!=null) {
if(m_replaceMissing.batchFinished()) {
Instance newInstance, instance;
while((instance=m_replaceMissing.output())!=null) {
if(!m_OutputFormatDefined)
setOutputFormat();
if(m_ntob!=null) {
m_ntob.input(instance);
}
else {
newInstance = convertInstance(instance);
push(newInstance);
}
}
if(m_ntob!=null) {
if(m_ntob.batchFinished()) {
//Instance newInstance, instance;
while((instance=m_ntob.output())!=null) {
if(!m_OutputFormatDefined)
setOutputFormat();
newInstance = convertInstance(instance);
push(newInstance);
}
m_ntob = null;
}
}
m_replaceMissing = null;
conversionDone=true;
}
}
if(conversionDone==false && m_ntob!=null) {
if(m_ntob.batchFinished()) {
Instance newInstance, instance;
while((instance=m_ntob.output())!=null) {
if(!m_OutputFormatDefined)
setOutputFormat();
newInstance = convertInstance(instance);
push(newInstance);
}
m_ntob = null;
}
}
m_OutputFormatDefined=false;
return super.batchFinished();
}
/** Sets the output format */
protected void setOutputFormat() {
Instances currentFormat;
if(m_ntob!=null) {
currentFormat = m_ntob.getOutputFormat();
}
else
currentFormat = getInputFormat();
if(m_percent>0)
{ m_k = (int) ((getInputFormat().numAttributes()-1)*m_percent);
// System.out.print("numAtts: "+currentFormat.numAttributes());
// System.out.print("percent: "+m_percent);
// System.out.print("percent*numAtts: "+(currentFormat.numAttributes()*m_percent));
// System.out.println("m_k: "+m_k);
}
Instances newFormat;
int newClassIndex=-1;
FastVector attributes = new FastVector();
for(int i=0; i<m_k; i++) {
attributes.addElement( new Attribute("K"+(i+1)) );
}
if(currentFormat.classIndex()!=-1) { //if classindex is set
//attributes.removeElementAt(attributes.size()-1);
attributes.addElement(currentFormat.attribute(currentFormat.classIndex()));
newClassIndex = attributes.size()-1;
}
newFormat = new Instances(currentFormat.relationName(), attributes, 0);
if(newClassIndex!=-1)
newFormat.setClassIndex(newClassIndex);
m_OutputFormatDefined=true;
m_random = new Random();
m_random.setSeed(m_rndmSeed);
m_rmatrix = new double[m_k][currentFormat.numAttributes()];
if(m_distribution==GAUSSIAN) {
for(int i=0; i<m_rmatrix.length; i++)
for(int j=0; j<m_rmatrix[i].length; j++)
m_rmatrix[i][j] = m_random.nextGaussian();
}
else {
boolean useDstrWithZero = (m_distribution==SPARSE1);
for(int i=0; i<m_rmatrix.length; i++)
for(int j=0; j<m_rmatrix[i].length; j++)
m_rmatrix[i][j] = rndmNum(useDstrWithZero);
}
setOutputFormat(newFormat);
}
/**
* converts a single instance to the required format
*
* @param currentInstance the instance to convert
* @return the converted instance
*/
protected Instance convertInstance(Instance currentInstance) {
Instance newInstance;
double vals[] = new double[getOutputFormat().numAttributes()];
int classIndex = (m_ntob==null) ? getInputFormat().classIndex():m_ntob.getOutputFormat().classIndex();
for(int i = 0; i < m_k; i++) {
vals[i] = computeRandomProjection(i,classIndex,currentInstance);
}
if (classIndex != -1) {
vals[m_k] = currentInstance.value(classIndex);
}
newInstance = new DenseInstance(currentInstance.weight(), vals);
newInstance.setDataset(getOutputFormat());
return newInstance;
}
/**
* computes one random projection for a given instance (skip missing values)
*
* @param rpIndex offset the new random projection attribute
* @param classIndex classIndex of the input instance
* @param instance the instance to convert
* @return the random sum
*/
protected double computeRandomProjection(int rpIndex, int classIndex, Instance instance) {
double sum = 0.0;
for(int i = 0; i < instance.numValues(); i++) {
int index = instance.index(i);
if (index != classIndex) {
double value = instance.valueSparse(i);
if (!Utils.isMissingValue(value)) {
sum += m_rmatrix[rpIndex][index] * value;
}
}
}
return sum;
}
private static final int weights[] = {1, 1, 4};
private static final int vals[] = {-1, 1, 0};
private static final int weights2[] = {1, 1};
private static final int vals2[] = {-1, 1};
private static final double sqrt3 = Math.sqrt(3);
/**
* returns a double x such that <br/>
* x = sqrt(3) * { -1 with prob. 1/6, 0 with prob. 2/3, 1 with prob. 1/6 }
*
* @param useDstrWithZero
* @return the generated number
*/
protected double rndmNum(boolean useDstrWithZero) {
if(useDstrWithZero)
return sqrt3 * vals[weightedDistribution(weights)];
else
return vals2[weightedDistribution(weights2)];
}
/**
* Calculates a weighted distribution
*
* @param weights the weights to use
* @return
*/
protected int weightedDistribution(int [] weights) {
int sum=0;
for(int i=0; i<weights.length; i++)
sum += weights[i];
int val = (int)Math.floor(m_random.nextDouble()*sum);
for(int i=0; i<weights.length; i++) {
val -= weights[i];
if(val<0)
return i;
}
return -1;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter:
* use -h for help
*/
public static void main(String [] argv) {
runFilter(new RandomProjection(), argv);
}
}