/*
* 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.
*/
/*
* DensityBasedClustererSplitEvaluator.java
* Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import weka.clusterers.ClusterEvaluation;
import weka.clusterers.Clusterer;
import weka.clusterers.AbstractClusterer;
import weka.clusterers.AbstractDensityBasedClusterer;
import weka.clusterers.DensityBasedClusterer;
import weka.clusterers.EM;
import weka.core.AdditionalMeasureProducer;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;
import java.io.ObjectStreamClass;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;
/**
* A SplitEvaluator that produces results for a density based clusterer.
*
* -W classname <br>
* Specify the full class name of the clusterer to evaluate. <p>
*
* @author Mark Hall (mhall{[at]}pentaho{[dot]}org
* @version $Revision: 5563 $
*/
public class DensityBasedClustererSplitEvaluator
implements SplitEvaluator,
OptionHandler,
AdditionalMeasureProducer,
RevisionHandler {
/** Remove the class column (if set) from the data */
protected boolean m_removeClassColumn = true;
/** The clusterer used for evaluation */
protected DensityBasedClusterer m_clusterer = new EM();
/** The names of any additional measures to look for in SplitEvaluators */
protected String [] m_additionalMeasures = null;
/** Array of booleans corresponding to the measures in m_AdditionalMeasures
indicating which of the AdditionalMeasures the current clusterer
can produce */
protected boolean [] m_doesProduce = null;
/** The number of additional measures that need to be filled in
after taking into account column constraints imposed by the final
destination for results */
protected int m_numberAdditionalMeasures = 0;
/** Holds the statistics for the most recent application of the clusterer */
protected String m_result = null;
/** The clusterer options (if any) */
protected String m_clustererOptions = "";
/** The clusterer version */
protected String m_clustererVersion = "";
/** The length of a key */
private static final int KEY_SIZE = 3;
/** The length of a result */
private static final int RESULT_SIZE = 6;
public DensityBasedClustererSplitEvaluator() {
updateOptions();
}
/**
* Returns a string describing this split evaluator
* @return a description of the split evaluator suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return " A SplitEvaluator that produces results for a density based clusterer. ";
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(1);
newVector.addElement(new Option(
"\tThe full class name of the density based clusterer.\n"
+"\teg: weka.clusterers.EM",
"W", 1,
"-W <class name>"));
if ((m_clusterer != null) &&
(m_clusterer instanceof OptionHandler)) {
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to clusterer "
+ m_clusterer.getClass().getName() + ":"));
Enumeration enu = ((OptionHandler)m_clusterer).listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -W classname <br>
* Specify the full class name of the clusterer to evaluate. <p>
*
* All option after -- will be passed to the classifier.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String cName = Utils.getOption('W', options);
if (cName.length() == 0) {
throw new Exception("A clusterer must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// Classifier.
setClusterer((DensityBasedClusterer)AbstractClusterer.forName(cName, null));
if (getClusterer() instanceof OptionHandler) {
((OptionHandler) getClusterer())
.setOptions(Utils.partitionOptions(options));
updateOptions();
}
}
/**
* Gets the current settings of the Classifier.
*
* @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 + 3];
int current = 0;
if (getClusterer() != null) {
options[current++] = "-W";
options[current++] = getClusterer().getClass().getName();
}
options[current++] = "--";
System.arraycopy(clustererOptions, 0, options, current,
clustererOptions.length);
current += clustererOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Set a list of method names for additional measures to look for
* in Classifiers. This could contain many measures (of which only a
* subset may be produceable by the current Classifier) if an experiment
* is the type that iterates over a set of properties.
* @param additionalMeasures a list of method names
*/
public void setAdditionalMeasures(String [] additionalMeasures) {
// System.err.println("ClassifierSplitEvaluator: setting additional measures");
m_additionalMeasures = additionalMeasures;
// determine which (if any) of the additional measures this clusterer
// can produce
if (m_additionalMeasures != null && m_additionalMeasures.length > 0) {
m_doesProduce = new boolean [m_additionalMeasures.length];
if (m_clusterer instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer)m_clusterer).
enumerateMeasures();
while (en.hasMoreElements()) {
String mname = (String)en.nextElement();
for (int j=0;j<m_additionalMeasures.length;j++) {
if (mname.compareToIgnoreCase(m_additionalMeasures[j]) == 0) {
m_doesProduce[j] = true;
}
}
}
}
} else {
m_doesProduce = null;
}
}
/**
* Returns an enumeration of any additional measure names that might be
* in the classifier
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector();
if (m_clusterer instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer)m_clusterer).
enumerateMeasures();
while (en.hasMoreElements()) {
String mname = (String)en.nextElement();
newVector.addElement(mname);
}
}
return newVector.elements();
}
/**
* Returns the value of the named measure
* @param additionalMeasureName the name of the measure to query for its value
* @return the value of the named measure
* @exception IllegalArgumentException if the named measure is not supported
*/
public double getMeasure(String additionalMeasureName) {
if (m_clusterer instanceof AdditionalMeasureProducer) {
return ((AdditionalMeasureProducer)m_clusterer).
getMeasure(additionalMeasureName);
} else {
throw new IllegalArgumentException("DensityBasedClustererSplitEvaluator: "
+"Can't return value for : "+additionalMeasureName
+". "+m_clusterer.getClass().getName()+" "
+"is not an AdditionalMeasureProducer");
}
}
/**
* Gets the data types of each of the key columns produced for a single run.
* The number of key fields must be constant
* for a given SplitEvaluator.
*
* @return an array containing objects of the type of each key column. The
* objects should be Strings, or Doubles.
*/
public Object [] getKeyTypes() {
Object [] keyTypes = new Object[KEY_SIZE];
keyTypes[0] = "";
keyTypes[1] = "";
keyTypes[2] = "";
return keyTypes;
}
/**
* Gets the names of each of the key columns produced for a single run.
* The number of key fields must be constant
* for a given SplitEvaluator.
*
* @return an array containing the name of each key column
*/
public String [] getKeyNames() {
String [] keyNames = new String[KEY_SIZE];
keyNames[0] = "Scheme";
keyNames[1] = "Scheme_options";
keyNames[2] = "Scheme_version_ID";
return keyNames;
}
/**
* Gets the key describing the current SplitEvaluator. For example
* This may contain the name of the classifier used for classifier
* predictive evaluation. The number of key fields must be constant
* for a given SplitEvaluator.
*
* @return an array of objects containing the key.
*/
public Object [] getKey(){
Object [] key = new Object[KEY_SIZE];
key[0] = m_clusterer.getClass().getName();
key[1] = m_clustererOptions;
key[2] = m_clustererVersion;
return key;
}
/**
* Gets the data types of each of the result columns produced for a
* single run. The number of result fields must be constant
* for a given SplitEvaluator.
*
* @return an array containing objects of the type of each result column.
* The objects should be Strings, or Doubles.
*/
public Object [] getResultTypes() {
int addm = (m_additionalMeasures != null)
? m_additionalMeasures.length
: 0;
int overall_length = RESULT_SIZE+addm;
Object [] resultTypes = new Object[overall_length];
Double doub = new Double(0);
int current = 0;
// number of training and testing instances
resultTypes[current++] = doub;
resultTypes[current++] = doub;
// log liklihood
resultTypes[current++] = doub;
// number of clusters
resultTypes[current++] = doub;
// timing stats
resultTypes[current++] = doub;
resultTypes[current++] = doub;
// resultTypes[current++] = "";
// add any additional measures
for (int i=0;i<addm;i++) {
resultTypes[current++] = doub;
}
if (current != overall_length) {
throw new Error("ResultTypes didn't fit RESULT_SIZE");
}
return resultTypes;
}
/**
* Gets the names of each of the result columns produced for a single run.
* The number of result fields must be constant
* for a given SplitEvaluator.
*
* @return an array containing the name of each result column
*/
public String [] getResultNames() {
int addm = (m_additionalMeasures != null)
? m_additionalMeasures.length
: 0;
int overall_length = RESULT_SIZE+addm;
String [] resultNames = new String[overall_length];
int current = 0;
resultNames[current++] = "Number_of_training_instances";
resultNames[current++] = "Number_of_testing_instances";
// Basic performance stats
resultNames[current++] = "Log_likelihood";
resultNames[current++] = "Number_of_clusters";
// Timing stats
resultNames[current++] = "Time_training";
resultNames[current++] = "Time_testing";
// Classifier defined extras
// resultNames[current++] = "Summary";
// add any additional measures
for (int i=0;i<addm;i++) {
resultNames[current++] = m_additionalMeasures[i];
}
if (current != overall_length) {
throw new Error("ResultNames didn't fit RESULT_SIZE");
}
return resultNames;
}
/**
* Gets the results for the supplied train and test datasets.
*
* @param train the training Instances.
* @param test the testing Instances.
* @return the results stored in an array. The objects stored in
* the array may be Strings, Doubles, or null (for the missing value).
* @exception Exception if a problem occurs while getting the results
*/
public Object [] getResult(Instances train, Instances test)
throws Exception {
if (m_clusterer == null) {
throw new Exception("No clusterer has been specified");
}
int addm = (m_additionalMeasures != null)
? m_additionalMeasures.length
: 0;
int overall_length = RESULT_SIZE+addm;
if (m_removeClassColumn && train.classIndex() != -1) {
// remove the class column from the training and testing data
Remove r = new Remove();
r.setAttributeIndicesArray(new int [] {train.classIndex()});
r.setInvertSelection(false);
r.setInputFormat(train);
train = Filter.useFilter(train, r);
test = Filter.useFilter(test, r);
}
train.setClassIndex(-1);
test.setClassIndex(-1);
ClusterEvaluation eval = new ClusterEvaluation();
Object [] result = new Object[overall_length];
long trainTimeStart = System.currentTimeMillis();
m_clusterer.buildClusterer(train);
double numClusters = m_clusterer.numberOfClusters();
eval.setClusterer(m_clusterer);
long trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
long testTimeStart = System.currentTimeMillis();
eval.evaluateClusterer(test);
long testTimeElapsed = System.currentTimeMillis() - testTimeStart;
// m_result = eval.toSummaryString();
// The results stored are all per instance -- can be multiplied by the
// number of instances to get absolute numbers
int current = 0;
result[current++] = new Double(train.numInstances());
result[current++] = new Double(test.numInstances());
result[current++] = new Double(eval.getLogLikelihood());
result[current++] = new Double(numClusters);
// Timing stats
result[current++] = new Double(trainTimeElapsed / 1000.0);
result[current++] = new Double(testTimeElapsed / 1000.0);
for (int i=0;i<addm;i++) {
if (m_doesProduce[i]) {
try {
double dv = ((AdditionalMeasureProducer)m_clusterer).
getMeasure(m_additionalMeasures[i]);
Double value = new Double(dv);
result[current++] = value;
} catch (Exception ex) {
System.err.println(ex);
}
} else {
result[current++] = null;
}
}
if (current != overall_length) {
throw new Error("Results didn't fit RESULT_SIZE");
}
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 removeClassColumnTipText() {
return "Remove the class column (if set) from the data.";
}
/**
* Set whether the class column should be removed from the data.
*
* @param r true if the class column is to be removed.
*/
public void setRemoveClassColumn(boolean r) {
m_removeClassColumn = r;
}
/**
* Get whether the class column is to be removed.
*
* @return true if the class column is to be removed.
*/
public boolean getRemoveClassColumn() {
return m_removeClassColumn;
}
/**
* 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 density based clusterer to use.";
}
/**
* Get the value of clusterer
*
* @return Value of clusterer.
*/
public DensityBasedClusterer getClusterer() {
return m_clusterer;
}
/**
* Sets the clusterer.
*
* @param newClusterer the new clusterer to use.
*/
public void setClusterer(DensityBasedClusterer newClusterer) {
m_clusterer = newClusterer;
updateOptions();
}
protected void updateOptions() {
if (m_clusterer instanceof OptionHandler) {
m_clustererOptions = Utils.joinOptions(((OptionHandler)m_clusterer)
.getOptions());
} else {
m_clustererOptions = "";
}
if (m_clusterer instanceof Serializable) {
ObjectStreamClass obs = ObjectStreamClass.lookup(m_clusterer
.getClass());
m_clustererVersion = "" + obs.getSerialVersionUID();
} else {
m_clustererVersion = "";
}
}
/**
* Set the Clusterer to use, given it's class name. A new clusterer will be
* instantiated.
*
* @param newClustererName the clusterer class name.
* @exception Exception if the class name is invalid.
*/
public void setClustererName(String newClustererName) throws Exception {
try {
setClusterer((DensityBasedClusterer)Class.forName(newClustererName)
.newInstance());
} catch (Exception ex) {
throw new Exception("Can't find Clusterer with class name: "
+ newClustererName);
}
}
/**
* Gets the raw output from the classifier
* @return the raw output from the classifier
*/
public String getRawResultOutput() {
StringBuffer result = new StringBuffer();
if (m_clusterer == null) {
return "<null> clusterer";
}
result.append(toString());
result.append("Clustering model: \n"+m_clusterer.toString()+'\n');
// append the performance statistics
if (m_result != null) {
// result.append(m_result);
if (m_doesProduce != null) {
for (int i=0;i<m_doesProduce.length;i++) {
if (m_doesProduce[i]) {
try {
double dv = ((AdditionalMeasureProducer)m_clusterer).
getMeasure(m_additionalMeasures[i]);
Double value = new Double(dv);
result.append(m_additionalMeasures[i]+" : "+value+'\n');
} catch (Exception ex) {
System.err.println(ex);
}
}
}
}
}
return result.toString();
}
/**
* Returns a text description of the split evaluator.
*
* @return a text description of the split evaluator.
*/
public String toString() {
String result = "DensityBasedClustererSplitEvaluator: ";
if (m_clusterer == null) {
return result + "<null> clusterer";
}
return result + m_clusterer.getClass().getName() + " "
+ m_clustererOptions + "(version " + m_clustererVersion + ")";
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5563 $");
}
}