/*
* 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.
*/
/*
* AddCluster.java
* Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.attribute;
import weka.clusterers.AbstractClusterer;
import weka.clusterers.Clusterer;
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.SparseInstance;
import weka.core.Utils;
import weka.core.WekaException;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.ObjectInputStream;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.<br/>
* Either the clustering algorithm gets built with the first batch of data or one specifies are serialized clusterer model file to use instead.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <clusterer specification>
* Full class name of clusterer to use, followed
* by scheme options. eg:
* "weka.clusterers.SimpleKMeans -N 3"
* (default: weka.clusterers.SimpleKMeans)</pre>
*
* <pre> -serialized <file>
* Instead of building a clusterer on the data, one can also provide
* a serialized model and use that for adding the clusters.</pre>
*
* <pre> -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* </pre>
*
<!-- options-end -->
*
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 5987 $
*/
public class AddCluster
extends Filter
implements UnsupervisedFilter, OptionHandler {
/** for serialization. */
static final long serialVersionUID = 7414280611943807337L;
/** The clusterer used to do the cleansing. */
protected Clusterer m_Clusterer = new weka.clusterers.SimpleKMeans();
/** The file from which to load a serialized clusterer. */
protected File m_SerializedClustererFile = new File(System.getProperty("user.dir"));
/** The actual clusterer used to do the clustering. */
protected Clusterer m_ActualClusterer = null;
/** Range of attributes to ignore. */
protected Range m_IgnoreAttributesRange = null;
/** Filter for removing attributes. */
protected Filter m_removeAttributes = new Remove();
/**
* 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);
}
/**
* 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;
}
/**
* 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;
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) {
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");
Instances toFilter = getInputFormat();
if (!isFirstBatchDone()) {
// filter out attributes if necessary
Instances toFilterIgnoringAttributes = removeIgnored(toFilter);
// serialized model or build clusterer from scratch?
File file = getSerializedClustererFile();
if (!file.isDirectory()) {
ObjectInputStream ois = new ObjectInputStream(new FileInputStream(file));
m_ActualClusterer = (Clusterer) ois.readObject();
Instances header = null;
// let's see whether there's an Instances header stored as well
try {
header = (Instances) ois.readObject();
}
catch (Exception e) {
// ignored
}
ois.close();
// same dataset format?
if ((header != null) && (!header.equalHeaders(toFilterIgnoringAttributes)))
throw new WekaException(
"Training header of clusterer and filter dataset don't match:\n"
+ header.equalHeadersMsg(toFilterIgnoringAttributes));
}
else {
m_ActualClusterer = AbstractClusterer.makeCopy(m_Clusterer);
m_ActualClusterer.buildClusterer(toFilterIgnoringAttributes);
}
// create output dataset with new attribute
Instances filtered = new Instances(toFilter, 0);
FastVector nominal_values = new FastVector(m_ActualClusterer.numberOfClusters());
for (int i = 0; i < m_ActualClusterer.numberOfClusters(); i++) {
nominal_values.addElement("cluster" + (i+1));
}
filtered.insertAttributeAt(new Attribute("cluster", nominal_values),
filtered.numAttributes());
setOutputFormat(filtered);
}
// build new dataset
for (int i=0; i<toFilter.numInstances(); i++) {
convertInstance(toFilter.instance(i));
}
flushInput();
m_NewBatch = true;
m_FirstBatchDone = 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;
}
/**
* 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 {
Instance original, processed;
original = instance;
// copy values
double[] instanceVals = new double[instance.numAttributes()+1];
for(int j = 0; j < instance.numAttributes(); j++) {
instanceVals[j] = original.value(j);
}
Instance filteredI = null;
if (m_removeAttributes != null) {
m_removeAttributes.input(instance);
filteredI = m_removeAttributes.output();
} else {
filteredI = instance;
}
// add cluster to end
try {
instanceVals[instance.numAttributes()] = m_ActualClusterer.clusterInstance(filteredI);
}
catch (Exception e) {
// clusterer couldn't cluster instance -> missing
instanceVals[instance.numAttributes()] = Utils.missingValue();
}
// create new instance
if (original instanceof SparseInstance) {
processed = new SparseInstance(original.weight(), instanceVals);
} else {
processed = new DenseInstance(original.weight(), instanceVals);
}
processed.setDataset(instance.dataset());
copyValues(processed, false, instance.dataset(), getOutputFormat());
processed.setDataset(getOutputFormat());
push(processed);
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector result = new Vector();
result.addElement(new Option(
"\tFull class name of clusterer to use, followed\n"
+ "\tby scheme options. eg:\n"
+ "\t\t\"weka.clusterers.SimpleKMeans -N 3\"\n"
+ "\t(default: weka.clusterers.SimpleKMeans)",
"W", 1, "-W <clusterer specification>"));
result.addElement(new Option(
"\tInstead of building a clusterer on the data, one can also provide\n"
+ "\ta serialized model and use that for adding the clusters.",
"serialized", 1, "-serialized <file>"));
result.addElement(new Option(
"\tThe range of attributes the clusterer should ignore.\n",
"I", 1,"-I <att1,att2-att4,...>"));
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <clusterer specification>
* Full class name of clusterer to use, followed
* by scheme options. eg:
* "weka.clusterers.SimpleKMeans -N 3"
* (default: weka.clusterers.SimpleKMeans)</pre>
*
* <pre> -serialized <file>
* Instead of building a clusterer on the data, one can also provide
* a serialized model and use that for adding the clusters.</pre>
*
* <pre> -I <att1,att2-att4,...>
* The range of attributes the clusterer should ignore.
* </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 tmpStr;
String[] tmpOptions;
File file;
boolean serializedModel;
serializedModel = false;
tmpStr = Utils.getOption("serialized", options);
if (tmpStr.length() != 0) {
file = new File(tmpStr);
if (!file.exists())
throw new FileNotFoundException(
"File '" + file.getAbsolutePath() + "' not found!");
if (file.isDirectory())
throw new FileNotFoundException(
"'" + file.getAbsolutePath() + "' points to a directory not a file!");
setSerializedClustererFile(file);
serializedModel = true;
}
else {
setSerializedClustererFile(null);
}
if (!serializedModel) {
tmpStr = Utils.getOption('W', options);
if (tmpStr.length() == 0)
tmpStr = weka.clusterers.SimpleKMeans.class.getName();
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length == 0) {
throw new Exception("Invalid clusterer specification string");
}
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setClusterer(AbstractClusterer.forName(tmpStr, tmpOptions));
}
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() {
Vector<String> result;
File file;
result = new Vector<String>();
file = getSerializedClustererFile();
if ((file != null) && (!file.isDirectory())) {
result.add("-serialized");
result.add(file.getAbsolutePath());
}
else {
result.add("-W");
result.add(getClustererSpec());
}
if (!getIgnoredAttributeIndices().equals("")) {
result.add("-I");
result.add(getIgnoredAttributeIndices());
}
return result.toArray(new String[result.size()]);
}
/**
* 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 adds a new nominal attribute representing the cluster "
+ "assigned to each instance by the specified clustering algorithm.\n"
+ "Either the clustering algorithm gets built with the first batch of "
+ "data or one specifies are serialized clusterer model file to use "
+ "instead.";
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String clustererTipText() {
return "The clusterer to assign clusters with.";
}
/**
* Sets the clusterer to assign clusters with.
*
* @param clusterer The clusterer to be used (with its options set).
*/
public void setClusterer(Clusterer clusterer) {
m_Clusterer = clusterer;
}
/**
* Gets the clusterer used by the filter.
*
* @return The clusterer being used.
*/
public Clusterer getClusterer() {
return m_Clusterer;
}
/**
* Gets the clusterer specification string, which contains the class name of
* the clusterer and any options to the clusterer.
*
* @return the clusterer string.
*/
protected String getClustererSpec() {
Clusterer c = getClusterer();
if (c instanceof OptionHandler) {
return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
}
return c.getClass().getName();
}
/**
* 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);
}
}
/**
* Gets the file pointing to a serialized, built clusterer. If it is
* null or pointing to a directory it will not be used.
*
* @return the file the serialized, built clusterer is located in
*/
public File getSerializedClustererFile() {
return m_SerializedClustererFile;
}
/**
* Sets the file pointing to a serialized, built clusterer. If the
* argument is null, doesn't exist or pointing to a directory, then the
* value is ignored.
*
* @param value the file pointing to the serialized, built clusterer
*/
public void setSerializedClustererFile(File value) {
if ((value == null) || (!value.exists()))
value = new File(System.getProperty("user.dir"));
m_SerializedClustererFile = value;
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String serializedClustererFileTipText() {
return "A file containing the serialized model of a built clusterer.";
}
/**
* 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 AddCluster(), argv);
}
}