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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 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 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.
*/
/*
* RegressionSplitEvaluator.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import java.io.ByteArrayOutputStream;
import java.io.ObjectOutputStream;
import java.io.ObjectStreamClass;
import java.io.Serializable;
import java.lang.management.ManagementFactory;
import java.lang.management.ThreadMXBean;
import java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.rules.ZeroR;
import weka.core.AdditionalMeasureProducer;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Summarizable;
import weka.core.Utils;
/**
* A SplitEvaluator that produces results for a
* classification scheme on a numeric class attribute.
*
*
*
* Valid options are:
*
*
*
* -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes
*
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
*
*
* @author Len Trigg ([email protected])
* @version $Revision: 11198 $
*/
public class RegressionSplitEvaluator implements SplitEvaluator, OptionHandler,
AdditionalMeasureProducer, RevisionHandler {
/** for serialization */
static final long serialVersionUID = -328181640503349202L;
/** The template classifier */
protected Classifier m_Template = new ZeroR();
/** The classifier used for evaluation */
protected Classifier m_Classifier;
/** 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 classifier can
* produce
*/
protected boolean[] m_doesProduce = null;
/** Holds the statistics for the most recent application of the classifier */
protected String m_result = null;
/** The classifier options (if any) */
protected String m_ClassifierOptions = "";
/** The classifier version */
protected String m_ClassifierVersion = "";
/** The length of a key */
private static final int KEY_SIZE = 3;
/** The length of a result */
private static final int RESULT_SIZE = 23;
/**
* No args constructor.
*/
public RegressionSplitEvaluator() {
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 classification "
+ "scheme on a numeric class attribute.";
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
@Override
public Enumeration listOptions() {
Vector newVector = new Vector(1);
newVector
.addElement(new Option("\tThe full class name of the classifier.\n"
+ "\teg: weka.classifiers.bayes.NaiveBayes", "W", 1, "-W "));
if ((m_Template != null) && (m_Template instanceof OptionHandler)) {
newVector.addElement(new Option("", "", 0,
"\nOptions specific to classifier " + m_Template.getClass().getName()
+ ":"));
Enumeration enu = ((OptionHandler) m_Template).listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options.
*
*
* Valid options are:
*
*
*
* -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes
*
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
*
*
* All option after -- will be passed to the classifier.
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
@Override
public void setOptions(String[] options) throws Exception {
String cName = Utils.getOption('W', options);
if (cName.length() > 0) {
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// Classifier.
setClassifier(Classifier.forName(cName, null));
}
if (getClassifier() instanceof OptionHandler) {
((OptionHandler) getClassifier()).setOptions(Utils
.partitionOptions(options));
updateOptions();
}
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
@Override
public String[] getOptions() {
String[] classifierOptions = new String[0];
if ((m_Template != null) && (m_Template instanceof OptionHandler)) {
classifierOptions = ((OptionHandler) m_Template).getOptions();
}
String[] options = new String[classifierOptions.length + 3];
int current = 0;
if (getClassifier() != null) {
options[current++] = "-W";
options[current++] = getClassifier().getClass().getName();
}
options[current++] = "--";
System.arraycopy(classifierOptions, 0, options, current,
classifierOptions.length);
current += classifierOptions.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 an array of method names.
*/
@Override
public void setAdditionalMeasures(String[] additionalMeasures) {
m_AdditionalMeasures = additionalMeasures;
// determine which (if any) of the additional measures this classifier
// can produce
if (m_AdditionalMeasures != null && m_AdditionalMeasures.length > 0) {
m_doesProduce = new boolean[m_AdditionalMeasures.length];
if (m_Template instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer) m_Template)
.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
*/
@Override
public Enumeration enumerateMeasures() {
Vector newVector = new Vector();
if (m_Template instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer) m_Template)
.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
* @throws IllegalArgumentException if the named measure is not supported
*/
@Override
public double getMeasure(String additionalMeasureName) {
if (m_Template instanceof AdditionalMeasureProducer) {
if (m_Classifier == null) {
throw new IllegalArgumentException("ClassifierSplitEvaluator: "
+ "Can't return result for measure, "
+ "classifier has not been built yet.");
}
return ((AdditionalMeasureProducer) m_Classifier)
.getMeasure(additionalMeasureName);
} else {
throw new IllegalArgumentException("ClassifierSplitEvaluator: "
+ "Can't return value for : " + additionalMeasureName + ". "
+ m_Template.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.
*/
@Override
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
*/
@Override
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.
*/
@Override
public Object[] getKey() {
Object[] key = new Object[KEY_SIZE];
key[0] = m_Template.getClass().getName();
key[1] = m_ClassifierOptions;
key[2] = m_ClassifierVersion;
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.
*/
@Override
public Object[] getResultTypes() {
int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
Object[] resultTypes = new Object[RESULT_SIZE + addm];
Double doub = new Double(0);
int current = 0;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
// Timing stats
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
// sizes
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = "";
// add any additional measures
for (int i = 0; i < addm; i++) {
resultTypes[current++] = doub;
}
if (current != RESULT_SIZE + addm) {
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
*/
@Override
public String[] getResultNames() {
int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
String[] resultNames = new String[RESULT_SIZE + addm];
int current = 0;
resultNames[current++] = "Number_of_training_instances";
resultNames[current++] = "Number_of_testing_instances";
// Sensitive stats - certainty of predictions
resultNames[current++] = "Mean_absolute_error";
resultNames[current++] = "Root_mean_squared_error";
resultNames[current++] = "Relative_absolute_error";
resultNames[current++] = "Root_relative_squared_error";
resultNames[current++] = "Correlation_coefficient";
resultNames[current++] = "Number_unclassified";
resultNames[current++] = "Percent_unclassified";
// SF stats
resultNames[current++] = "SF_prior_entropy";
resultNames[current++] = "SF_scheme_entropy";
resultNames[current++] = "SF_entropy_gain";
resultNames[current++] = "SF_mean_prior_entropy";
resultNames[current++] = "SF_mean_scheme_entropy";
resultNames[current++] = "SF_mean_entropy_gain";
// Timing stats
resultNames[current++] = "Elapsed_Time_training";
resultNames[current++] = "Elapsed_Time_testing";
resultNames[current++] = "UserCPU_Time_training";
resultNames[current++] = "UserCPU_Time_testing";
// sizes
resultNames[current++] = "Serialized_Model_Size";
resultNames[current++] = "Serialized_Train_Set_Size";
resultNames[current++] = "Serialized_Test_Set_Size";
// Classifier defined extras
resultNames[current++] = "Summary";
// add any additional measures
for (int i = 0; i < addm; i++) {
resultNames[current++] = m_AdditionalMeasures[i];
}
if (current != RESULT_SIZE + addm) {
throw new Error("ResultNames didn't fit RESULT_SIZE");
}
return resultNames;
}
/**
* Gets the results for the supplied train and test datasets. Now performs a
* deep copy of the classifier before it is built and evaluated (just in case
* the classifier is not initialized properly in buildClassifier()).
*
* @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).
* @throws Exception if a problem occurs while getting the results
*/
@Override
public Object[] getResult(Instances train, Instances test) throws Exception {
if (train.classAttribute().type() != Attribute.NUMERIC) {
throw new Exception("Class attribute is not numeric!");
}
if (m_Template == null) {
throw new Exception("No classifier has been specified");
}
ThreadMXBean thMonitor = ManagementFactory.getThreadMXBean();
boolean canMeasureCPUTime = thMonitor.isThreadCpuTimeSupported();
if (canMeasureCPUTime && !thMonitor.isThreadCpuTimeEnabled()) {
thMonitor.setThreadCpuTimeEnabled(true);
}
int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
Object[] result = new Object[RESULT_SIZE + addm];
long thID = Thread.currentThread().getId();
long CPUStartTime = -1, trainCPUTimeElapsed = -1, testCPUTimeElapsed = -1, trainTimeStart, trainTimeElapsed, testTimeStart, testTimeElapsed;
Evaluation eval = new Evaluation(train);
m_Classifier = Classifier.makeCopy(m_Template);
trainTimeStart = System.currentTimeMillis();
if (canMeasureCPUTime) {
CPUStartTime = thMonitor.getThreadUserTime(thID);
}
m_Classifier.buildClassifier(train);
if (canMeasureCPUTime) {
trainCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
}
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
testTimeStart = System.currentTimeMillis();
if (canMeasureCPUTime) {
CPUStartTime = thMonitor.getThreadUserTime(thID);
}
eval.evaluateModel(m_Classifier, test);
if (canMeasureCPUTime) {
testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
}
testTimeElapsed = System.currentTimeMillis() - testTimeStart;
thMonitor = null;
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(eval.numInstances());
result[current++] = new Double(eval.meanAbsoluteError());
result[current++] = new Double(eval.rootMeanSquaredError());
result[current++] = new Double(eval.relativeAbsoluteError());
result[current++] = new Double(eval.rootRelativeSquaredError());
result[current++] = new Double(eval.correlationCoefficient());
result[current++] = new Double(eval.unclassified());
result[current++] = new Double(eval.pctUnclassified());
result[current++] = new Double(eval.SFPriorEntropy());
result[current++] = new Double(eval.SFSchemeEntropy());
result[current++] = new Double(eval.SFEntropyGain());
result[current++] = new Double(eval.SFMeanPriorEntropy());
result[current++] = new Double(eval.SFMeanSchemeEntropy());
result[current++] = new Double(eval.SFMeanEntropyGain());
// Timing stats
result[current++] = new Double(trainTimeElapsed / 1000.0);
result[current++] = new Double(testTimeElapsed / 1000.0);
if (canMeasureCPUTime) {
result[current++] =
new Double((trainCPUTimeElapsed / 1000000.0) / 1000.0);
result[current++] = new Double((testCPUTimeElapsed / 1000000.0) / 1000.0);
} else {
result[current++] = new Double(Instance.missingValue());
result[current++] = new Double(Instance.missingValue());
}
// sizes
ByteArrayOutputStream bastream = new ByteArrayOutputStream();
ObjectOutputStream oostream = new ObjectOutputStream(bastream);
oostream.writeObject(m_Classifier);
result[current++] = new Double(bastream.size());
bastream = new ByteArrayOutputStream();
oostream = new ObjectOutputStream(bastream);
oostream.writeObject(train);
result[current++] = new Double(bastream.size());
bastream = new ByteArrayOutputStream();
oostream = new ObjectOutputStream(bastream);
oostream.writeObject(test);
result[current++] = new Double(bastream.size());
if (m_Classifier instanceof Summarizable) {
result[current++] = ((Summarizable) m_Classifier).toSummaryString();
} else {
result[current++] = null;
}
for (int i = 0; i < addm; i++) {
if (m_doesProduce[i]) {
try {
double dv = ((AdditionalMeasureProducer) m_Classifier)
.getMeasure(m_AdditionalMeasures[i]);
if (!Instance.isMissingValue(dv)) {
Double value = new Double(dv);
result[current++] = value;
} else {
result[current++] = null;
}
} catch (Exception ex) {
System.err.println(ex);
}
} else {
result[current++] = null;
}
}
if (current != RESULT_SIZE + addm) {
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 classifierTipText() {
return "The classifier to use.";
}
/**
* Get the value of Classifier.
*
* @return Value of Classifier.
*/
public Classifier getClassifier() {
return m_Template;
}
/**
* Sets the classifier.
*
* @param newClassifier the new classifier to use.
*/
public void setClassifier(Classifier newClassifier) {
m_Template = newClassifier;
updateOptions();
System.err.println("RegressionSplitEvaluator: In set classifier");
}
/**
* Updates the options that the current classifier is using.
*/
protected void updateOptions() {
if (m_Template instanceof OptionHandler) {
m_ClassifierOptions = Utils.joinOptions(((OptionHandler) m_Template)
.getOptions());
} else {
m_ClassifierOptions = "";
}
if (m_Template instanceof Serializable) {
ObjectStreamClass obs = ObjectStreamClass.lookup(m_Template.getClass());
m_ClassifierVersion = "" + obs.getSerialVersionUID();
} else {
m_ClassifierVersion = "";
}
}
/**
* Set the Classifier to use, given it's class name. A new classifier will be
* instantiated.
*
* @param newClassifierName the Classifier class name.
* @throws Exception if the class name is invalid.
*/
public void setClassifierName(String newClassifierName) throws Exception {
try {
setClassifier((Classifier) Class.forName(newClassifierName).newInstance());
} catch (Exception ex) {
throw new Exception("Can't find Classifier with class name: "
+ newClassifierName);
}
}
/**
* Gets the raw output from the classifier
*
* @return the raw output from the classifier
*/
@Override
public String getRawResultOutput() {
StringBuffer result = new StringBuffer();
if (m_Classifier == null) {
return " classifier";
}
result.append(toString());
result.append("Classifier model: \n" + m_Classifier.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_Classifier)
.getMeasure(m_AdditionalMeasures[i]);
if (!Instance.isMissingValue(dv)) {
Double value = new Double(dv);
result.append(m_AdditionalMeasures[i] + " : " + value + '\n');
} else {
result.append(m_AdditionalMeasures[i] + " : " + '?' + '\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.
*/
@Override
public String toString() {
String result = "RegressionSplitEvaluator: ";
if (m_Template == null) {
return result + " classifier";
}
return result + m_Template.getClass().getName() + " " + m_ClassifierOptions
+ "(version " + m_ClassifierVersion + ")";
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 11198 $");
}
} // RegressionSplitEvaluator
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