/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* ClusterMembership.java
* Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.attribute;
import weka.clusterers.DensityBasedClusterer;
import weka.clusterers.AbstractDensityBasedClusterer;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.DenseInstance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data). If a (nominal) class attribute is set, the clusterer is run separately for each class. The class attribute (if set) and any user-specified attributes are ignored during the clustering operation
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <clusterer name>
* Full name of clusterer to use. eg:
* weka.clusterers.EM
* Additional options after the '--'.
* (default: weka.clusterers.EM)</pre>
*
* <pre> -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* (the class attribute is automatically ignored)</pre>
*
<!-- options-end -->
*
* Options after the -- are passed on to the clusterer.
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @author Eibe Frank
* @version $Revision: 5987 $
*/
public class ClusterMembership
extends Filter
implements UnsupervisedFilter, OptionHandler {
/** for serialization */
static final long serialVersionUID = 6675702504667714026L;
/** The clusterer */
protected DensityBasedClusterer m_clusterer = new weka.clusterers.EM();
/** Array for storing the clusterers */
protected DensityBasedClusterer[] m_clusterers;
/** Range of attributes to ignore */
protected Range m_ignoreAttributesRange;
/** Filter for removing attributes */
protected Filter m_removeAttributes;
/** The prior probability for each class */
protected double[] m_priors;
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result = m_clusterer.getCapabilities();
result.setMinimumNumberInstances(0);
return result;
}
/**
* Returns the Capabilities of this filter, makes sure that the class is
* never set (for the clusterer).
*
* @param data the data to use for customization
* @return the capabilities of this object, based on the data
* @see #getCapabilities()
*/
public Capabilities getCapabilities(Instances data) {
Instances newData;
newData = new Instances(data, 0);
newData.setClassIndex(-1);
return super.getCapabilities(newData);
}
/**
* tests the data whether the filter can actually handle it
*
* @param instanceInfo the data to test
* @throws Exception if the test fails
*/
protected void testInputFormat(Instances instanceInfo) throws Exception {
getCapabilities(instanceInfo).testWithFail(removeIgnored(instanceInfo));
}
/**
* 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 inputFormat can't be set successfully
*/
public boolean setInputFormat(Instances instanceInfo) throws Exception {
super.setInputFormat(instanceInfo);
m_removeAttributes = null;
m_priors = null;
return false;
}
/**
* filters all attributes that should be ignored
*
* @param data the data to filter
* @return the filtered data
* @throws Exception if filtering fails
*/
protected Instances removeIgnored(Instances data) throws Exception {
Instances result = data;
if (m_ignoreAttributesRange != null || data.classIndex() >= 0) {
result = new Instances(data);
m_removeAttributes = new Remove();
String rangeString = "";
if (m_ignoreAttributesRange != null) {
rangeString += m_ignoreAttributesRange.getRanges();
}
if (data.classIndex() >= 0) {
if (rangeString.length() > 0) {
rangeString += "," + (data.classIndex() + 1);
} else {
rangeString = "" + (data.classIndex() + 1);
}
}
((Remove) m_removeAttributes).setAttributeIndices(rangeString);
((Remove) m_removeAttributes).setInvertSelection(false);
m_removeAttributes.setInputFormat(data);
result = Filter.useFilter(data, m_removeAttributes);
}
return result;
}
/**
* Signify that this batch of input to the filter is finished.
*
* @return true if there are instances pending output
* @throws IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (outputFormatPeek() == null) {
Instances toFilter = getInputFormat();
Instances[] toFilterIgnoringAttributes;
// Make subsets if class is nominal
if ((toFilter.classIndex() >= 0) && toFilter.classAttribute().isNominal()) {
toFilterIgnoringAttributes = new Instances[toFilter.numClasses()];
for (int i = 0; i < toFilter.numClasses(); i++) {
toFilterIgnoringAttributes[i] = new Instances(toFilter, toFilter.numInstances());
}
for (int i = 0; i < toFilter.numInstances(); i++) {
toFilterIgnoringAttributes[(int)toFilter.instance(i).classValue()].add(toFilter.instance(i));
}
m_priors = new double[toFilter.numClasses()];
for (int i = 0; i < toFilter.numClasses(); i++) {
toFilterIgnoringAttributes[i].compactify();
m_priors[i] = toFilterIgnoringAttributes[i].sumOfWeights();
}
Utils.normalize(m_priors);
} else {
toFilterIgnoringAttributes = new Instances[1];
toFilterIgnoringAttributes[0] = toFilter;
m_priors = new double[1];
m_priors[0] = 1;
}
// filter out attributes if necessary
for (int i = 0; i < toFilterIgnoringAttributes.length; i++)
toFilterIgnoringAttributes[i] = removeIgnored(toFilterIgnoringAttributes[i]);
// build the clusterers
if ((toFilter.classIndex() <= 0) || !toFilter.classAttribute().isNominal()) {
m_clusterers = AbstractDensityBasedClusterer.makeCopies(m_clusterer, 1);
m_clusterers[0].buildClusterer(toFilterIgnoringAttributes[0]);
} else {
m_clusterers = AbstractDensityBasedClusterer.makeCopies(m_clusterer, toFilter.numClasses());
for (int i = 0; i < m_clusterers.length; i++) {
if (toFilterIgnoringAttributes[i].numInstances() == 0) {
m_clusterers[i] = null;
} else {
m_clusterers[i].buildClusterer(toFilterIgnoringAttributes[i]);
}
}
}
// create output dataset
FastVector attInfo = new FastVector();
for (int j = 0; j < m_clusterers.length; j++) {
if (m_clusterers[j] != null) {
for (int i = 0; i < m_clusterers[j].numberOfClusters(); i++) {
attInfo.addElement(new Attribute("pCluster_" + j + "_" + i));
}
}
}
if (toFilter.classIndex() >= 0) {
attInfo.addElement(toFilter.classAttribute().copy());
}
attInfo.trimToSize();
Instances filtered = new Instances(toFilter.relationName()+"_clusterMembership",
attInfo, 0);
if (toFilter.classIndex() >= 0) {
filtered.setClassIndex(filtered.numAttributes() - 1);
}
setOutputFormat(filtered);
// build new dataset
for (int i = 0; i < toFilter.numInstances(); i++) {
convertInstance(toFilter.instance(i));
}
}
flushInput();
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* Input an instance for filtering. Ordinarily the instance is processed
* and made available for output immediately. Some filters require all
* instances be read before producing output.
*
* @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 defined.
*/
public boolean input(Instance instance) throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (outputFormatPeek() != null) {
convertInstance(instance);
return true;
}
bufferInput(instance);
return false;
}
/**
* Converts logs back to density values.
*
* @param j the index of the clusterer
* @param in the instance to convert the logs back
* @return the densities
* @throws Exception if something goes wrong
*/
protected double[] logs2densities(int j, Instance in) throws Exception {
double[] logs = m_clusterers[j].logJointDensitiesForInstance(in);
for (int i = 0; i < logs.length; i++) {
logs[i] += Math.log(m_priors[j]);
}
return logs;
}
/**
* Convert a single instance over. The converted instance is added to
* the end of the output queue.
*
* @param instance the instance to convert
* @throws Exception if something goes wrong
*/
protected void convertInstance(Instance instance) throws Exception {
// set up values
double [] instanceVals = new double[outputFormatPeek().numAttributes()];
double [] tempvals;
if (instance.classIndex() >= 0) {
tempvals = new double[outputFormatPeek().numAttributes() - 1];
} else {
tempvals = new double[outputFormatPeek().numAttributes()];
}
int pos = 0;
for (int j = 0; j < m_clusterers.length; j++) {
if (m_clusterers[j] != null) {
double [] probs;
if (m_removeAttributes != null) {
m_removeAttributes.input(instance);
probs = logs2densities(j, m_removeAttributes.output());
} else {
probs = logs2densities(j, instance);
}
System.arraycopy(probs, 0, tempvals, pos, probs.length);
pos += probs.length;
}
}
tempvals = Utils.logs2probs(tempvals);
System.arraycopy(tempvals, 0, instanceVals, 0, tempvals.length);
if (instance.classIndex() >= 0) {
instanceVals[instanceVals.length - 1] = instance.classValue();
}
push(new DenseInstance(instance.weight(), instanceVals));
}
/**
* 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("\tFull name of clusterer to use. eg:\n"
+ "\t\tweka.clusterers.EM\n"
+ "\tAdditional options after the '--'.\n"
+ "\t(default: weka.clusterers.EM)",
"W", 1, "-W <clusterer name>"));
newVector.
addElement(new Option("\tThe range of attributes the clusterer should ignore."
+"\n\t(the class attribute is automatically ignored)",
"I", 1,"-I <att1,att2-att4,...>"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <clusterer name>
* Full name of clusterer to use. eg:
* weka.clusterers.EM
* Additional options after the '--'.
* (default: weka.clusterers.EM)</pre>
*
* <pre> -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* (the class attribute is automatically ignored)</pre>
*
<!-- options-end -->
*
* Options after the -- are passed on to the clusterer.
*
* @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 clustererString = Utils.getOption('W', options);
if (clustererString.length() == 0)
clustererString = weka.clusterers.EM.class.getName();
setDensityBasedClusterer((DensityBasedClusterer)Utils.
forName(DensityBasedClusterer.class, clustererString,
Utils.partitionOptions(options)));
setIgnoredAttributeIndices(Utils.getOption('I', options));
Utils.checkForRemainingOptions(options);
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] clustererOptions = new String [0];
if ((m_clusterer != null) &&
(m_clusterer instanceof OptionHandler)) {
clustererOptions = ((OptionHandler)m_clusterer).getOptions();
}
String [] options = new String [clustererOptions.length + 5];
int current = 0;
if (!getIgnoredAttributeIndices().equals("")) {
options[current++] = "-I";
options[current++] = getIgnoredAttributeIndices();
}
if (m_clusterer != null) {
options[current++] = "-W";
options[current++] = getDensityBasedClusterer().getClass().getName();
}
options[current++] = "--";
System.arraycopy(clustererOptions, 0, options, current,
clustererOptions.length);
current += clustererOptions.length;
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 "A filter that uses a density-based clusterer to generate cluster "
+ "membership values; filtered instances are composed of these values "
+ "plus the class attribute (if set in the input data). If a (nominal) "
+ "class attribute is set, the clusterer is run separately for each "
+ "class. The class attribute (if set) and any user-specified "
+ "attributes are ignored during the clustering operation";
}
/**
* Returns a description of this option suitable for display
* as a tip text in the gui.
*
* @return description of this option
*/
public String densityBasedClustererTipText() {
return "The clusterer that will generate membership values for the instances.";
}
/**
* Set the clusterer for use in filtering
*
* @param newClusterer the clusterer to use
*/
public void setDensityBasedClusterer(DensityBasedClusterer newClusterer) {
m_clusterer = newClusterer;
}
/**
* Get the clusterer used by this filter
*
* @return the clusterer used
*/
public DensityBasedClusterer getDensityBasedClusterer() {
return m_clusterer;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String ignoredAttributeIndicesTipText() {
return "The range of attributes to be ignored by the clusterer. eg: first-3,5,9-last";
}
/**
* Gets ranges of attributes to be ignored.
*
* @return a string containing a comma-separated list of ranges
*/
public String getIgnoredAttributeIndices() {
if (m_ignoreAttributesRange == null) {
return "";
} else {
return m_ignoreAttributesRange.getRanges();
}
}
/**
* Sets the ranges of attributes to be ignored. If provided string
* is null, no attributes will be ignored.
*
* @param rangeList a string representing the list of attributes.
* eg: first-3,5,6-last
* @throws IllegalArgumentException if an invalid range list is supplied
*/
public void setIgnoredAttributeIndices(String rangeList) {
if ((rangeList == null) || (rangeList.length() == 0)) {
m_ignoreAttributesRange = null;
} else {
m_ignoreAttributesRange = new Range();
m_ignoreAttributesRange.setRanges(rangeList);
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5987 $");
}
/**
* 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 ClusterMembership(), argv);
}
}