/*
* 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.
*/
/*
* ClassifierSplitEvaluator.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import weka.classifiers.Classifier;
import weka.classifiers.AbstractClassifier;
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;
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;
/**
<!-- globalinfo-start -->
* A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -I <index>
* The index of an attribute to output in the
* results. This attribute should identify an
* instance in order to know which instances are
* in the test set of a cross validation. if 0
* no output (default 0).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* All options after -- will be passed to the classifier.
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 5987 $
*/
public class ClassifierSplitEvaluator
implements SplitEvaluator, OptionHandler, AdditionalMeasureProducer,
RevisionHandler {
/** for serialization */
static final long serialVersionUID = -8511241602760467265L;
/** 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;
/** 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 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 = 30;
/** The number of IR statistics */
private static final int NUM_IR_STATISTICS = 14;
/** The number of averaged IR statistics */
private static final int NUM_WEIGHTED_IR_STATISTICS = 8;
/** The number of unweighted averaged IR statistics */
private static final int NUM_UNWEIGHTED_IR_STATISTICS = 2;
/** Class index for information retrieval statistics (default 0) */
private int m_IRclass = 0;
/** Flag for prediction and target columns output.*/
private boolean m_predTargetColumn = false;
/** Attribute index of instance identifier (default -1) */
private int m_attID = -1;
/**
* No args constructor.
*/
public ClassifierSplitEvaluator() {
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 nominal class attribute.";
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(4);
newVector.addElement(new Option(
"\tThe full class name of the classifier.\n"
+"\teg: weka.classifiers.bayes.NaiveBayes",
"W", 1,
"-W <class name>"));
newVector.addElement(new Option(
"\tThe index of the class for which IR statistics\n" +
"\tare to be output. (default 1)",
"C", 1,
"-C <index>"));
newVector.addElement(new Option(
"\tThe index of an attribute to output in the\n" +
"\tresults. This attribute should identify an\n" +
"\tinstance in order to know which instances are\n" +
"\tin the test set of a cross validation. if 0\n" +
"\tno output (default 0).",
"I", 1,
"-I <index>"));
newVector.addElement(new Option(
"\tAdd target and prediction columns to the result\n" +
"\tfor each fold.",
"P", 0,
"-P"));
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. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -I <index>
* The index of an attribute to output in the
* results. This attribute should identify an
* instance in order to know which instances are
* in the test set of a cross validation. if 0
* no output (default 0).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* All options 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
*/
public void setOptions(String[] options) throws Exception {
String cName = Utils.getOption('W', options);
if (cName.length() == 0) {
throw new Exception("A classifier 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.
setClassifier(AbstractClassifier.forName(cName, null));
if (getClassifier() instanceof OptionHandler) {
((OptionHandler) getClassifier())
.setOptions(Utils.partitionOptions(options));
updateOptions();
}
String indexName = Utils.getOption('C', options);
if (indexName.length() != 0) {
m_IRclass = (new Integer(indexName)).intValue() - 1;
} else {
m_IRclass = 0;
}
String attID = Utils.getOption('I', options);
if (attID.length() != 0) {
m_attID = (new Integer(attID)).intValue() - 1;
} else {
m_attID = -1;
}
m_predTargetColumn = Utils.getFlag('P', options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
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 + 8];
int current = 0;
if (getClassifier() != null) {
options[current++] = "-W";
options[current++] = getClassifier().getClass().getName();
}
options[current++] = "-I";
options[current++] = "" + (m_attID + 1);
if (getPredTargetColumn()) options[current++] = "-P";
options[current++] = "-C";
options[current++] = "" + (m_IRclass + 1);
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 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 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
*/
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
*/
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.
*/
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_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.
*/
public Object [] getResultTypes() {
int addm = (m_AdditionalMeasures != null)
? m_AdditionalMeasures.length
: 0;
int overall_length = RESULT_SIZE+addm;
overall_length += NUM_IR_STATISTICS;
overall_length += NUM_WEIGHTED_IR_STATISTICS;
overall_length += NUM_UNWEIGHTED_IR_STATISTICS;
if (getAttributeID() >= 0) overall_length += 1;
if (getPredTargetColumn()) overall_length += 2;
Object [] resultTypes = new Object[overall_length];
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;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
resultTypes[current++] = doub;
// IR stats
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;
// Unweighted IR stats
resultTypes[current++] = doub;
resultTypes[current++] = doub;
// Weighted IR stats
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;
// Prediction interval statistics
resultTypes[current++] = doub;
resultTypes[current++] = doub;
// ID/Targets/Predictions
if (getAttributeID() >= 0) resultTypes[current++] = "";
if (getPredTargetColumn()){
resultTypes[current++] = "";
resultTypes[current++] = "";
}
// Classifier defined extras
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;
overall_length += NUM_IR_STATISTICS;
overall_length += NUM_WEIGHTED_IR_STATISTICS;
overall_length += NUM_UNWEIGHTED_IR_STATISTICS;
if (getAttributeID() >= 0) overall_length += 1;
if (getPredTargetColumn()) overall_length += 2;
String [] resultNames = new String[overall_length];
int current = 0;
resultNames[current++] = "Number_of_training_instances";
resultNames[current++] = "Number_of_testing_instances";
// Basic performance stats - right vs wrong
resultNames[current++] = "Number_correct";
resultNames[current++] = "Number_incorrect";
resultNames[current++] = "Number_unclassified";
resultNames[current++] = "Percent_correct";
resultNames[current++] = "Percent_incorrect";
resultNames[current++] = "Percent_unclassified";
resultNames[current++] = "Kappa_statistic";
// 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";
// 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";
// K&B stats
resultNames[current++] = "KB_information";
resultNames[current++] = "KB_mean_information";
resultNames[current++] = "KB_relative_information";
// IR stats
resultNames[current++] = "True_positive_rate";
resultNames[current++] = "Num_true_positives";
resultNames[current++] = "False_positive_rate";
resultNames[current++] = "Num_false_positives";
resultNames[current++] = "True_negative_rate";
resultNames[current++] = "Num_true_negatives";
resultNames[current++] = "False_negative_rate";
resultNames[current++] = "Num_false_negatives";
resultNames[current++] = "IR_precision";
resultNames[current++] = "IR_recall";
resultNames[current++] = "F_measure";
resultNames[current++] = "Area_under_ROC";
// Weighted IR stats
resultNames[current++] = "Weighted_avg_true_positive_rate";
resultNames[current++] = "Weighted_avg_false_positive_rate";
resultNames[current++] = "Weighted_avg_true_negative_rate";
resultNames[current++] = "Weighted_avg_false_negative_rate";
resultNames[current++] = "Weighted_avg_IR_precision";
resultNames[current++] = "Weighted_avg_IR_recall";
resultNames[current++] = "Weighted_avg_F_measure";
resultNames[current++] = "Weighted_avg_area_under_ROC";
// Unweighted IR stats
resultNames[current++] = "Unweighted_macro_avg_F_measure";
resultNames[current++] = "Unweighted_micro_avg_F_measure";
// 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";
// Prediction interval statistics
resultNames[current++] = "Coverage_of_Test_Cases_By_Regions";
resultNames[current++] = "Size_of_Predicted_Regions";
// ID/Targets/Predictions
if (getAttributeID() >= 0) resultNames[current++] = "Instance_ID";
if (getPredTargetColumn()){
resultNames[current++] = "Targets";
resultNames[current++] = "Predictions";
}
// 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. 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
*/
public Object [] getResult(Instances train, Instances test)
throws Exception {
if (train.classAttribute().type() != Attribute.NOMINAL) {
throw new Exception("Class attribute is not nominal!");
}
if (m_Template == null) {
throw new Exception("No classifier has been specified");
}
int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
int overall_length = RESULT_SIZE+addm;
overall_length += NUM_IR_STATISTICS;
overall_length += NUM_WEIGHTED_IR_STATISTICS;
overall_length += NUM_UNWEIGHTED_IR_STATISTICS;
if (getAttributeID() >= 0) overall_length += 1;
if (getPredTargetColumn()) overall_length += 2;
ThreadMXBean thMonitor = ManagementFactory.getThreadMXBean();
boolean canMeasureCPUTime = thMonitor.isThreadCpuTimeSupported();
if(!thMonitor.isThreadCpuTimeEnabled())
thMonitor.setThreadCpuTimeEnabled(true);
Object [] result = new Object[overall_length];
Evaluation eval = new Evaluation(train);
m_Classifier = AbstractClassifier.makeCopy(m_Template);
double [] predictions;
long thID = Thread.currentThread().getId();
long CPUStartTime=-1, trainCPUTimeElapsed=-1, testCPUTimeElapsed=-1,
trainTimeStart, trainTimeElapsed, testTimeStart, testTimeElapsed;
//training classifier
trainTimeStart = System.currentTimeMillis();
if(canMeasureCPUTime)
CPUStartTime = thMonitor.getThreadUserTime(thID);
m_Classifier.buildClassifier(train);
if(canMeasureCPUTime)
trainCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
//testing classifier
testTimeStart = System.currentTimeMillis();
if(canMeasureCPUTime)
CPUStartTime = thMonitor.getThreadUserTime(thID);
predictions = 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.correct());
result[current++] = new Double(eval.incorrect());
result[current++] = new Double(eval.unclassified());
result[current++] = new Double(eval.pctCorrect());
result[current++] = new Double(eval.pctIncorrect());
result[current++] = new Double(eval.pctUnclassified());
result[current++] = new Double(eval.kappa());
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.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());
// K&B stats
result[current++] = new Double(eval.KBInformation());
result[current++] = new Double(eval.KBMeanInformation());
result[current++] = new Double(eval.KBRelativeInformation());
// IR stats
result[current++] = new Double(eval.truePositiveRate(m_IRclass));
result[current++] = new Double(eval.numTruePositives(m_IRclass));
result[current++] = new Double(eval.falsePositiveRate(m_IRclass));
result[current++] = new Double(eval.numFalsePositives(m_IRclass));
result[current++] = new Double(eval.trueNegativeRate(m_IRclass));
result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
result[current++] = new Double(eval.precision(m_IRclass));
result[current++] = new Double(eval.recall(m_IRclass));
result[current++] = new Double(eval.fMeasure(m_IRclass));
result[current++] = new Double(eval.areaUnderROC(m_IRclass));
// Weighted IR stats
result[current++] = new Double(eval.weightedTruePositiveRate());
result[current++] = new Double(eval.weightedFalsePositiveRate());
result[current++] = new Double(eval.weightedTrueNegativeRate());
result[current++] = new Double(eval.weightedFalseNegativeRate());
result[current++] = new Double(eval.weightedPrecision());
result[current++] = new Double(eval.weightedRecall());
result[current++] = new Double(eval.weightedFMeasure());
result[current++] = new Double(eval.weightedAreaUnderROC());
// Unweighted IR stats
result[current++] = new Double(eval.unweightedMacroFmeasure());
result[current++] = new Double(eval.unweightedMicroFmeasure());
// 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(Utils.missingValue());
result[current++] = new Double(Utils.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());
// Prediction interval statistics
result[current++] = new Double(eval.coverageOfTestCasesByPredictedRegions());
result[current++] = new Double(eval.sizeOfPredictedRegions());
// IDs
if (getAttributeID() >= 0){
String idsString = "";
if (test.attribute(m_attID).isNumeric()){
if (test.numInstances() > 0)
idsString += test.instance(0).value(m_attID);
for(int i=1;i<test.numInstances();i++){
idsString += "|" + test.instance(i).value(m_attID);
}
} else {
if (test.numInstances() > 0)
idsString += test.instance(0).stringValue(m_attID);
for(int i=1;i<test.numInstances();i++){
idsString += "|" + test.instance(i).stringValue(m_attID);
}
}
result[current++] = idsString;
}
if (getPredTargetColumn()){
if (test.classAttribute().isNumeric()){
// Targets
if (test.numInstances() > 0){
String targetsString = "";
targetsString += test.instance(0).value(test.classIndex());
for(int i=1;i<test.numInstances();i++){
targetsString += "|" + test.instance(i).value(test.classIndex());
}
result[current++] = targetsString;
}
// Predictions
if (predictions.length > 0){
String predictionsString = "";
predictionsString += predictions[0];
for(int i=1;i<predictions.length;i++){
predictionsString += "|" + predictions[i];
}
result[current++] = predictionsString;
}
} else {
// Targets
if (test.numInstances() > 0){
String targetsString = "";
targetsString += test.instance(0).stringValue(test.classIndex());
for(int i=1;i<test.numInstances();i++){
targetsString += "|" + test.instance(i).stringValue(test.classIndex());
}
result[current++] = targetsString;
}
// Predictions
if (predictions.length > 0){
String predictionsString = "";
predictionsString += test.classAttribute().value((int) predictions[0]);
for(int i=1;i<predictions.length;i++){
predictionsString += "|" + test.classAttribute().value((int) predictions[i]);
}
result[current++] = predictionsString;
}
}
}
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 (!Utils.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 != 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 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();
}
/**
* Get the value of ClassForIRStatistics.
* @return Value of ClassForIRStatistics.
*/
public int getClassForIRStatistics() {
return m_IRclass;
}
/**
* Set the value of ClassForIRStatistics.
* @param v Value to assign to ClassForIRStatistics.
*/
public void setClassForIRStatistics(int v) {
m_IRclass = v;
}
/**
* Get the index of Attibute Identifying the instances
* @return index of outputed Attribute.
*/
public int getAttributeID() {
return m_attID;
}
/**
* Set the index of Attibute Identifying the instances
* @param v index the attribute to output
*/
public void setAttributeID(int v) {
m_attID = v;
}
/**
*@return true if the prediction and target columns must be outputed.
*/
public boolean getPredTargetColumn(){
return m_predTargetColumn;
}
/**
* Set the flag for prediction and target output.
*@param v true if the 2 columns have to be outputed. false otherwise.
*/
public void setPredTargetColumn(boolean v){
m_predTargetColumn = v;
}
/**
* 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 th,0e classifier
*/
public String getRawResultOutput() {
StringBuffer result = new StringBuffer();
if (m_Classifier == null) {
return "<null> 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 (!Utils.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.
*/
public String toString() {
String result = "ClassifierSplitEvaluator: ";
if (m_Template == null) {
return result + "<null> classifier";
}
return result + m_Template.getClass().getName() + " "
+ m_ClassifierOptions + "(version " + m_ClassifierVersion + ")";
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5987 $");
}
} // ClassifierSplitEvaluator