/*
* 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.
*/
/*
* FilteredClusterer.java
* Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
*
*/
package weka.clusterers;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.SupervisedFilter;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter. Like the clusterer, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -F <filter specification>
* Full class name of filter to use, followed
* by filter options.
* eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
* (default: weka.filters.AllFilter)</pre>
*
* <pre> -W
* Full name of base clusterer.
* (default: weka.clusterers.SimpleKMeans)</pre>
*
* <pre>
* Options specific to clusterer weka.clusterers.SimpleKMeans:
* </pre>
*
* <pre> -N <num>
* number of clusters.
* (default 2).</pre>
*
* <pre> -V
* Display std. deviations for centroids.
* </pre>
*
* <pre> -M
* Replace missing values with mean/mode.
* </pre>
*
* <pre> -S <num>
* Random number seed.
* (default 10)</pre>
*
<!-- options-end -->
*
* Based on code from the FilteredClassifier by Len Trigg.
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 5488 $
* @see weka.classifiers.meta.FilteredClassifier
*/
public class FilteredClusterer
extends SingleClustererEnhancer {
/** for serialization. */
private static final long serialVersionUID = 1420005943163412943L;
/** The filter. */
protected Filter m_Filter;
/** The instance structure of the filtered instances. */
protected Instances m_FilteredInstances;
/**
* Default constructor.
*/
public FilteredClusterer() {
m_Clusterer = new SimpleKMeans();
m_Filter = new weka.filters.AllFilter();
}
/**
* Returns a string describing this clusterer.
*
* @return a description of the clusterer suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Class for running an arbitrary clusterer on data that has been passed "
+ "through an arbitrary filter. Like the clusterer, the structure of the filter "
+ "is based exclusively on the training data and test instances will be processed "
+ "by the filter without changing their structure.";
}
/**
* String describing default filter.
*
* @return the default filter classname
*/
protected String defaultFilterString() {
return weka.filters.AllFilter.class.getName();
}
/**
* 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 filter to use, followed\n"
+ "\tby filter options.\n"
+ "\teg: \"weka.filters.unsupervised.attribute.Remove -V -R 1,2\"\n"
+ "(default: " + defaultFilterString() + ")",
"F", 1, "-F <filter specification>"));
Enumeration enm = super.listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -F <filter specification>
* Full class name of filter to use, followed
* by filter options.
* eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
* (default: weka.filters.AllFilter)</pre>
*
* <pre> -W
* Full name of base clusterer.
* (default: weka.clusterers.SimpleKMeans)</pre>
*
* <pre>
* Options specific to clusterer weka.clusterers.SimpleKMeans:
* </pre>
*
* <pre> -N <num>
* number of clusters.
* (default 2).</pre>
*
* <pre> -V
* Display std. deviations for centroids.
* </pre>
*
* <pre> -M
* Replace missing values with mean/mode.
* </pre>
*
* <pre> -S <num>
* Random number seed.
* (default 10)</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;
tmpStr = Utils.getOption('F', options);
if (tmpStr.length() > 0) {
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length == 0)
throw new IllegalArgumentException("Invalid filter specification string");
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setFilter((Filter) Utils.forName(Filter.class, tmpStr, tmpOptions));
}
else {
setFilter(new weka.filters.AllFilter());
}
super.setOptions(options);
}
/**
* Gets the current settings of the clusterer.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector result;
String[] options;
int i;
result = new Vector();
result.add("-F");
result.add(getFilterSpec());
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String filterTipText() {
return "The filter to be used.";
}
/**
* Sets the filter.
*
* @param filter the filter with all options set.
*/
public void setFilter(Filter filter) {
m_Filter = filter;
if (m_Filter instanceof SupervisedFilter)
System.out.println(
"WARNING: you are using a supervised filter, which will leak "
+ "information about the class attribute!");
}
/**
* Gets the filter used.
*
* @return the filter
*/
public Filter getFilter() {
return m_Filter;
}
/**
* Gets the filter specification string, which contains the class name of
* the filter and any options to the filter.
*
* @return the filter string.
*/
protected String getFilterSpec() {
String result;
Filter filter;
filter = getFilter();
result = filter.getClass().getName();
if (filter instanceof OptionHandler)
result += " " + Utils.joinOptions(((OptionHandler) filter).getOptions());
return result;
}
/**
* Returns default capabilities of the clusterer.
*
* @return the capabilities of this clusterer
*/
public Capabilities getCapabilities() {
Capabilities result;
if (getFilter() == null) {
result = super.getCapabilities();
result.disableAll();
result.enable(Capability.NO_CLASS);
} else {
result = getFilter().getCapabilities();
}
// set dependencies
for (Capability cap: Capability.values())
result.enableDependency(cap);
return result;
}
/**
* Build the clusterer on the filtered data.
*
* @param data the training data
* @throws Exception if the clusterer could not be built successfully
*/
public void buildClusterer(Instances data) throws Exception {
if (m_Clusterer == null)
throw new Exception("No base clusterer has been set!");
// remove instances with missing class
if (data.classIndex() > -1) {
data = new Instances(data);
data.deleteWithMissingClass();
}
m_Filter.setInputFormat(data); // filter capabilities are checked here
data = Filter.useFilter(data, m_Filter);
// can clusterer handle the data?
getClusterer().getCapabilities().testWithFail(data);
m_FilteredInstances = data.stringFreeStructure();
m_Clusterer.buildClusterer(data);
}
/**
* Classifies a given instance after filtering.
*
* @param instance the instance to be classified
* @return the class distribution for the given instance
* @throws Exception if instance could not be classified
* successfully
*/
public double[] distributionForInstance(Instance instance)
throws Exception {
if (m_Filter.numPendingOutput() > 0)
throw new Exception("Filter output queue not empty!");
if (!m_Filter.input(instance))
throw new Exception(
"Filter didn't make the test instance immediately available!");
m_Filter.batchFinished();
Instance newInstance = m_Filter.output();
return m_Clusterer.distributionForInstance(newInstance);
}
/**
* Output a representation of this clusterer.
*
* @return a representation of this clusterer
*/
public String toString() {
String result;
if (m_FilteredInstances == null)
result = "FilteredClusterer: No model built yet.";
else
result = "FilteredClusterer using "
+ getClustererSpec()
+ " on data filtered through "
+ getFilterSpec()
+ "\n\nFiltered Header\n"
+ m_FilteredInstances.toString()
+ "\n\nClusterer Model\n"
+ m_Clusterer.toString();
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5488 $");
}
/**
* Main method for testing this class.
*
* @param args the commandline options, use "-h" for help
*/
public static void main(String [] args) {
runClusterer(new FilteredClusterer(), args);
}
}