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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other breaking updates.
/*
* 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 .
*/
/*
* ClusterMembership.java
* Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.attribute;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;
import weka.clusterers.AbstractDensityBasedClusterer;
import weka.clusterers.DensityBasedClusterer;
import weka.core.*;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
/**
* 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
*
*
*
* Valid options are:
*
*
*
* -W <clusterer name>
* Full name of clusterer to use. eg:
* weka.clusterers.EM
* Additional options after the '--'.
* (default: weka.clusterers.EM)
*
*
*
* -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* (the class attribute is automatically ignored)
*
*
*
*
* Options after the -- are passed on to the clusterer.
*
* @author Mark Hall ([email protected])
* @author Eibe Frank
* @version $Revision: 14534 $
*/
public class ClusterMembership extends Filter implements UnsupervisedFilter,
OptionHandler, WeightedInstancesHandler, WeightedAttributesHandler {
/** 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
*/
@Override
public Capabilities getCapabilities() {
Capabilities result = m_clusterer.getCapabilities();
result.enableAllClasses();
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()
*/
@Override
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
*/
@Override
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
*/
@Override
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
*/
@Override
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
ArrayList attInfo = new ArrayList();
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.add(new Attribute("pCluster_" + j + "_" + i));
}
}
}
if (toFilter.classIndex() >= 0) {
attInfo.add((Attribute) 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.
*/
@Override
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.
*/
@Override
public Enumeration