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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.
*/
/*
* CheckClassifier.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers;
import weka.core.Attribute;
import weka.core.CheckScheme;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SerializationHelper;
import weka.core.TestInstances;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
* Class for examining the capabilities and finding problems with
* classifiers. If you implement a classifier using the WEKA.libraries,
* you should run the checks on it to ensure robustness and correct
* operation. Passing all the tests of this object does not mean
* bugs in the classifier don't exist, but this will help find some
* common ones.
*
* Typical usage:
* java weka.classifiers.CheckClassifier -W classifier_name
* classifier_options
*
* CheckClassifier reports on the following:
*
* - Classifier abilities
*
* - Possible command line options to the classifier
* - Whether the classifier can predict nominal, numeric, string,
* date or relational class attributes. Warnings will be displayed if
* performance is worse than ZeroR
* - Whether the classifier can be trained incrementally
* - Whether the classifier can handle numeric predictor attributes
* - Whether the classifier can handle nominal predictor attributes
* - Whether the classifier can handle string predictor attributes
* - Whether the classifier can handle date predictor attributes
* - Whether the classifier can handle relational predictor attributes
* - Whether the classifier can handle multi-instance data
* - Whether the classifier can handle missing predictor values
* - Whether the classifier can handle missing class values
* - Whether a nominal classifier only handles 2 class problems
* - Whether the classifier can handle instance weights
*
*
* - Correct functioning
*
* - Correct initialisation during buildClassifier (i.e. no result
* changes when buildClassifier called repeatedly)
* - Whether incremental training produces the same results
* as during non-incremental training (which may or may not
* be OK)
* - Whether the classifier alters the data pased to it
* (number of instances, instance order, instance weights, etc)
* - Whether the toString() method works correctly before the
* classifier has been built.
*
*
* - Degenerate cases
*
* - building classifier with zero training instances
* - all but one predictor attribute values missing
* - all predictor attribute values missing
* - all but one class values missing
* - all class values missing
*
*
*
* Running CheckClassifier with the debug option set will output the
* training and test datasets for any failed tests.
*
* The weka.classifiers.AbstractClassifierTest uses this
* class to test all the classifiers. Any changes here, have to be
* checked in that abstract test class, too.
*
* Valid options are:
*
* -D
* Turn on debugging output.
*
* -S
* Silent mode - prints nothing to stdout.
*
* -N <num>
* The number of instances in the datasets (default 20).
*
* -nominal <num>
* The number of nominal attributes (default 2).
*
* -nominal-values <num>
* The number of values for nominal attributes (default 1).
*
* -numeric <num>
* The number of numeric attributes (default 1).
*
* -string <num>
* The number of string attributes (default 1).
*
* -date <num>
* The number of date attributes (default 1).
*
* -relational <num>
* The number of relational attributes (default 1).
*
* -num-instances-relational <num>
* The number of instances in relational/bag attributes (default 10).
*
* -words <comma-separated-list>
* The words to use in string attributes.
*
* -word-separators <chars>
* The word separators to use in string attributes.
*
* -W
* Full name of the classifier analysed.
* eg: weka.classifiers.bayes.NaiveBayes
* (default weka.classifiers.rules.ZeroR)
*
*
* 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
*
*
* Options after -- are passed to the designated classifier.
*
* @author Len Trigg ([email protected])
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 1.33 $
* @see TestInstances
*/
public class CheckClassifier
extends CheckScheme {
/*
* Note about test methods:
* - methods return array of booleans
* - first index: success or not
* - second index: acceptable or not (e.g., Exception is OK)
* - in case the performance is worse than that of ZeroR both indices are true
*
* FracPete (fracpete at waikato dot ac dot nz)
*/
/*** The classifier to be examined */
protected Classifier m_Classifier = new weka.classifiers.rules.ZeroR();
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector result = new Vector();
Enumeration en = super.listOptions();
while (en.hasMoreElements())
result.addElement(en.nextElement());
result.addElement(new Option(
"\tFull name of the classifier analysed.\n"
+"\teg: weka.classifiers.bayes.NaiveBayes\n"
+ "\t(default weka.classifiers.rules.ZeroR)",
"W", 1, "-W"));
if ((m_Classifier != null)
&& (m_Classifier instanceof OptionHandler)) {
result.addElement(new Option("", "", 0,
"\nOptions specific to classifier "
+ m_Classifier.getClass().getName()
+ ":"));
Enumeration enu = ((OptionHandler)m_Classifier).listOptions();
while (enu.hasMoreElements())
result.addElement(enu.nextElement());
}
return result.elements();
}
/**
* Parses a given list of options.
*
* Valid options are:
*
* -D
* Turn on debugging output.
*
* -S
* Silent mode - prints nothing to stdout.
*
* -N <num>
* The number of instances in the datasets (default 20).
*
* -nominal <num>
* The number of nominal attributes (default 2).
*
* -nominal-values <num>
* The number of values for nominal attributes (default 1).
*
* -numeric <num>
* The number of numeric attributes (default 1).
*
* -string <num>
* The number of string attributes (default 1).
*
* -date <num>
* The number of date attributes (default 1).
*
* -relational <num>
* The number of relational attributes (default 1).
*
* -num-instances-relational <num>
* The number of instances in relational/bag attributes (default 10).
*
* -words <comma-separated-list>
* The words to use in string attributes.
*
* -word-separators <chars>
* The word separators to use in string attributes.
*
* -W
* Full name of the classifier analysed.
* eg: weka.classifiers.bayes.NaiveBayes
* (default weka.classifiers.rules.ZeroR)
*
*
* 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
*
*
* @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;
super.setOptions(options);
tmpStr = Utils.getOption('W', options);
if (tmpStr.length() == 0)
tmpStr = weka.classifiers.rules.ZeroR.class.getName();
setClassifier(
(Classifier) forName(
"weka.classifiers",
Classifier.class,
tmpStr,
Utils.partitionOptions(options)));
}
/**
* Gets the current settings of the CheckClassifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector result;
String[] options;
int i;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
if (getClassifier() != null) {
result.add("-W");
result.add(getClassifier().getClass().getName());
}
if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler))
options = ((OptionHandler) m_Classifier).getOptions();
else
options = new String[0];
if (options.length > 0) {
result.add("--");
for (i = 0; i < options.length; i++)
result.add(options[i]);
}
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Begin the tests, reporting results to System.out
*/
public void doTests() {
if (getClassifier() == null) {
println("\n=== No classifier set ===");
return;
}
println("\n=== Check on Classifier: "
+ getClassifier().getClass().getName()
+ " ===\n");
// Start tests
m_ClasspathProblems = false;
println("--> Checking for interfaces");
canTakeOptions();
boolean updateableClassifier = updateableClassifier()[0];
boolean weightedInstancesHandler = weightedInstancesHandler()[0];
boolean multiInstanceHandler = multiInstanceHandler()[0];
println("--> Classifier tests");
declaresSerialVersionUID();
testToString();
testsPerClassType(Attribute.NOMINAL, updateableClassifier, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.NUMERIC, updateableClassifier, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.DATE, updateableClassifier, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.STRING, updateableClassifier, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.RELATIONAL, updateableClassifier, weightedInstancesHandler, multiInstanceHandler);
}
/**
* Set the classifier for boosting.
*
* @param newClassifier the Classifier to use.
*/
public void setClassifier(Classifier newClassifier) {
m_Classifier = newClassifier;
}
/**
* Get the classifier used as the classifier
*
* @return the classifier used as the classifier
*/
public Classifier getClassifier() {
return m_Classifier;
}
/**
* Run a battery of tests for a given class attribute type
*
* @param classType true if the class attribute should be numeric
* @param updateable true if the classifier is updateable
* @param weighted true if the classifier says it handles weights
* @param multiInstance true if the classifier is a multi-instance classifier
*/
protected void testsPerClassType(int classType,
boolean updateable,
boolean weighted,
boolean multiInstance) {
boolean PNom = canPredict(true, false, false, false, false, multiInstance, classType)[0];
boolean PNum = canPredict(false, true, false, false, false, multiInstance, classType)[0];
boolean PStr = canPredict(false, false, true, false, false, multiInstance, classType)[0];
boolean PDat = canPredict(false, false, false, true, false, multiInstance, classType)[0];
boolean PRel;
if (!multiInstance)
PRel = canPredict(false, false, false, false, true, multiInstance, classType)[0];
else
PRel = false;
if (PNom || PNum || PStr || PDat || PRel) {
if (weighted)
instanceWeights(PNom, PNum, PStr, PDat, PRel, multiInstance, classType);
canHandleOnlyClass(PNom, PNum, PStr, PDat, PRel, classType);
if (classType == Attribute.NOMINAL)
canHandleNClasses(PNom, PNum, PStr, PDat, PRel, multiInstance, 4);
if (!multiInstance) {
canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 0);
canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 1);
}
canHandleZeroTraining(PNom, PNum, PStr, PDat, PRel, multiInstance, classType);
boolean handleMissingPredictors = canHandleMissing(PNom, PNum, PStr, PDat, PRel,
multiInstance, classType,
true, false, 20)[0];
if (handleMissingPredictors)
canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, true, false, 100);
boolean handleMissingClass = canHandleMissing(PNom, PNum, PStr, PDat, PRel,
multiInstance, classType,
false, true, 20)[0];
if (handleMissingClass)
canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, false, true, 100);
correctBuildInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance, classType);
datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, classType,
handleMissingPredictors, handleMissingClass);
doesntUseTestClassVal(PNom, PNum, PStr, PDat, PRel, multiInstance, classType);
if (updateable)
updatingEquality(PNom, PNum, PStr, PDat, PRel, multiInstance, classType);
}
}
/**
* Checks whether the scheme's toString() method works even though the
* classifies hasn't been built yet.
*
* @return index 0 is true if the toString() method works fine
*/
protected boolean[] testToString() {
boolean[] result = new boolean[2];
print("toString...");
try {
Classifier copy = (Classifier) m_Classifier.getClass().newInstance();
copy.toString();
result[0] = true;
println("yes");
}
catch (Exception e) {
result[0] = false;
println("no");
if (m_Debug) {
println("\n=== Full report ===");
e.printStackTrace();
println("\n");
}
}
return result;
}
/**
* tests for a serialVersionUID. Fails in case the scheme doesn't declare
* a UID.
*
* @return index 0 is true if the scheme declares a UID
*/
protected boolean[] declaresSerialVersionUID() {
boolean[] result = new boolean[2];
print("serialVersionUID...");
result[0] = !SerializationHelper.needsUID(m_Classifier.getClass());
if (result[0])
println("yes");
else
println("no");
return result;
}
/**
* Checks whether the scheme can take command line options.
*
* @return index 0 is true if the classifier can take options
*/
protected boolean[] canTakeOptions() {
boolean[] result = new boolean[2];
print("options...");
if (m_Classifier instanceof OptionHandler) {
println("yes");
if (m_Debug) {
println("\n=== Full report ===");
Enumeration enu = ((OptionHandler)m_Classifier).listOptions();
while (enu.hasMoreElements()) {
Option option = (Option) enu.nextElement();
print(option.synopsis() + "\n"
+ option.description() + "\n");
}
println("\n");
}
result[0] = true;
}
else {
println("no");
result[0] = false;
}
return result;
}
/**
* Checks whether the scheme can build models incrementally.
*
* @return index 0 is true if the classifier can train incrementally
*/
protected boolean[] updateableClassifier() {
boolean[] result = new boolean[2];
print("updateable classifier...");
if (m_Classifier instanceof UpdateableClassifier) {
println("yes");
result[0] = true;
}
else {
println("no");
result[0] = false;
}
return result;
}
/**
* Checks whether the scheme says it can handle instance weights.
*
* @return true if the classifier handles instance weights
*/
protected boolean[] weightedInstancesHandler() {
boolean[] result = new boolean[2];
print("weighted instances classifier...");
if (m_Classifier instanceof WeightedInstancesHandler) {
println("yes");
result[0] = true;
}
else {
println("no");
result[0] = false;
}
return result;
}
/**
* Checks whether the scheme handles multi-instance data.
*
* @return true if the classifier handles multi-instance data
*/
protected boolean[] multiInstanceHandler() {
boolean[] result = new boolean[2];
print("multi-instance classifier...");
if (m_Classifier instanceof MultiInstanceCapabilitiesHandler) {
println("yes");
result[0] = true;
}
else {
println("no");
result[0] = false;
}
return result;
}
/**
* Checks basic prediction of the scheme, for simple non-troublesome
* datasets.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NOMINAL, NUMERIC, etc.)
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
*/
protected boolean[] canPredict(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
print("basic predict");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
FastVector accepts = new FastVector();
accepts.addElement("unary");
accepts.addElement("binary");
accepts.addElement("nominal");
accepts.addElement("numeric");
accepts.addElement("string");
accepts.addElement("date");
accepts.addElement("relational");
accepts.addElement("multi-instance");
accepts.addElement("not in classpath");
int numTrain = getNumInstances(), numTest = getNumInstances(),
numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numTest, numClasses,
accepts);
}
/**
* Checks whether the scheme can handle data that contains only the class
* attribute. If a scheme cannot build a proper model with that data, it
* should default back to a ZeroR model.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param classType the class type (NOMINAL, NUMERIC, etc.)
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
*/
protected boolean[] canHandleOnlyClass(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
int classType) {
print("only class in data");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, false, classType);
print("...");
FastVector accepts = new FastVector();
accepts.addElement("class");
accepts.addElement("zeror");
int numTrain = getNumInstances(), numTest = getNumInstances(),
missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(false, false, false, false, false,
false,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numTest, 2,
accepts);
}
/**
* Checks whether nominal schemes can handle more than two classes.
* If a scheme is only designed for two-class problems it should
* throw an appropriate exception for multi-class problems.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param numClasses the number of classes to test
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
*/
protected boolean[] canHandleNClasses(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int numClasses) {
print("more than two class problems");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL);
print("...");
FastVector accepts = new FastVector();
accepts.addElement("number");
accepts.addElement("class");
int numTrain = getNumInstances(), numTest = getNumInstances(),
missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
Attribute.NOMINAL,
missingLevel, predictorMissing, classMissing,
numTrain, numTest, numClasses,
accepts);
}
/**
* Checks whether the scheme can handle class attributes as Nth attribute.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @param classIndex the index of the class attribute (0-based, -1 means last attribute)
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
* @see TestInstances#CLASS_IS_LAST
*/
protected boolean[] canHandleClassAsNthAttribute(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int classIndex) {
if (classIndex == TestInstances.CLASS_IS_LAST)
print("class attribute as last attribute");
else
print("class attribute as " + (classIndex + 1) + ". attribute");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
FastVector accepts = new FastVector();
int numTrain = getNumInstances(), numTest = getNumInstances(), numClasses = 2,
missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
classIndex,
missingLevel, predictorMissing, classMissing,
numTrain, numTest, numClasses,
accepts);
}
/**
* Checks whether the scheme can handle zero training instances.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
*/
protected boolean[] canHandleZeroTraining(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
print("handle zero training instances");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
FastVector accepts = new FastVector();
accepts.addElement("train");
accepts.addElement("value");
int numTrain = 0, numTest = getNumInstances(), numClasses = 2,
missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(
nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numTest, numClasses,
accepts);
}
/**
* Checks whether the scheme correctly initialises models when
* buildClassifier is called. This test calls buildClassifier with
* one training dataset and records performance on a test set.
* buildClassifier is then called on a training set with different
* structure, and then again with the original training set. The
* performance on the test set is compared with the original results
* and any performance difference noted as incorrect build initialisation.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @return index 0 is true if the test was passed, index 1 is true if the
* scheme performs worse than ZeroR, but without error (index 0 is
* false)
*/
protected boolean[] correctBuildInitialisation(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
boolean[] result = new boolean[2];
print("correct initialisation during buildClassifier");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = getNumInstances(), numTest = getNumInstances(),
numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
Instances train1 = null;
Instances test1 = null;
Instances train2 = null;
Instances test2 = null;
Classifier classifier = null;
Evaluation evaluation1A = null;
Evaluation evaluation1B = null;
Evaluation evaluation2 = null;
boolean built = false;
int stage = 0;
try {
// Make two sets of train/test splits with different
// numbers of attributes
train1 = makeTestDataset(42, numTrain,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
train2 = makeTestDataset(84, numTrain,
nominalPredictor ? getNumNominal() + 1 : 0,
numericPredictor ? getNumNumeric() + 1 : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
test1 = makeTestDataset(24, numTest,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
test2 = makeTestDataset(48, numTest,
nominalPredictor ? getNumNominal() + 1 : 0,
numericPredictor ? getNumNumeric() + 1 : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
if (missingLevel > 0) {
addMissing(train1, missingLevel, predictorMissing, classMissing);
addMissing(test1, Math.min(missingLevel,50), predictorMissing,
classMissing);
addMissing(train2, missingLevel, predictorMissing, classMissing);
addMissing(test2, Math.min(missingLevel,50), predictorMissing,
classMissing);
}
classifier = Classifier.makeCopies(getClassifier(), 1)[0];
evaluation1A = new Evaluation(train1);
evaluation1B = new Evaluation(train1);
evaluation2 = new Evaluation(train2);
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
stage = 0;
classifier.buildClassifier(train1);
built = true;
if (!testWRTZeroR(classifier, evaluation1A, train1, test1)[0]) {
throw new Exception("Scheme performs worse than ZeroR");
}
stage = 1;
built = false;
classifier.buildClassifier(train2);
built = true;
if (!testWRTZeroR(classifier, evaluation2, train2, test2)[0]) {
throw new Exception("Scheme performs worse than ZeroR");
}
stage = 2;
built = false;
classifier.buildClassifier(train1);
built = true;
if (!testWRTZeroR(classifier, evaluation1B, train1, test1)[0]) {
throw new Exception("Scheme performs worse than ZeroR");
}
stage = 3;
if (!evaluation1A.equals(evaluation1B)) {
if (m_Debug) {
println("\n=== Full report ===\n"
+ evaluation1A.toSummaryString("\nFirst buildClassifier()",
true)
+ "\n\n");
println(
evaluation1B.toSummaryString("\nSecond buildClassifier()",
true)
+ "\n\n");
}
throw new Exception("Results differ between buildClassifier calls");
}
println("yes");
result[0] = true;
if (false && m_Debug) {
println("\n=== Full report ===\n"
+ evaluation1A.toSummaryString("\nFirst buildClassifier()",
true)
+ "\n\n");
println(
evaluation1B.toSummaryString("\nSecond buildClassifier()",
true)
+ "\n\n");
}
}
catch (Exception ex) {
String msg = ex.getMessage().toLowerCase();
if (msg.indexOf("worse than zeror") >= 0) {
println("warning: performs worse than ZeroR");
result[0] = (stage < 1);
result[1] = (stage < 1);
} else {
println("no");
result[0] = false;
}
if (m_Debug) {
println("\n=== Full Report ===");
print("Problem during");
if (built) {
print(" testing");
} else {
print(" training");
}
switch (stage) {
case 0:
print(" of dataset 1");
break;
case 1:
print(" of dataset 2");
break;
case 2:
print(" of dataset 1 (2nd build)");
break;
case 3:
print(", comparing results from builds of dataset 1");
break;
}
println(": " + ex.getMessage() + "\n");
println("here are the datasets:\n");
println("=== Train1 Dataset ===\n"
+ train1.toString() + "\n");
println("=== Test1 Dataset ===\n"
+ test1.toString() + "\n\n");
println("=== Train2 Dataset ===\n"
+ train2.toString() + "\n");
println("=== Test2 Dataset ===\n"
+ test2.toString() + "\n\n");
}
}
return result;
}
/**
* Checks basic missing value handling of the scheme. If the missing
* values cause an exception to be thrown by the scheme, this will be
* recorded.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @param predictorMissing true if the missing values may be in
* the predictors
* @param classMissing true if the missing values may be in the class
* @param missingLevel the percentage of missing values
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
*/
protected boolean[] canHandleMissing(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
boolean predictorMissing,
boolean classMissing,
int missingLevel) {
if (missingLevel == 100)
print("100% ");
print("missing");
if (predictorMissing) {
print(" predictor");
if (classMissing)
print(" and");
}
if (classMissing)
print(" class");
print(" values");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
FastVector accepts = new FastVector();
accepts.addElement("missing");
accepts.addElement("value");
accepts.addElement("train");
int numTrain = getNumInstances(), numTest = getNumInstances(),
numClasses = 2;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numTest, numClasses,
accepts);
}
/**
* Checks whether an updateable scheme produces the same model when
* trained incrementally as when batch trained. The model itself
* cannot be compared, so we compare the evaluation on test data
* for both models. It is possible to get a false positive on this
* test (likelihood depends on the classifier).
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @return index 0 is true if the test was passed
*/
protected boolean[] updatingEquality(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
print("incremental training produces the same results"
+ " as batch training");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = getNumInstances(), numTest = getNumInstances(),
numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
boolean[] result = new boolean[2];
Instances train = null;
Instances test = null;
Classifier [] classifiers = null;
Evaluation evaluationB = null;
Evaluation evaluationI = null;
boolean built = false;
try {
train = makeTestDataset(42, numTrain,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
test = makeTestDataset(24, numTest,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
if (missingLevel > 0) {
addMissing(train, missingLevel, predictorMissing, classMissing);
addMissing(test, Math.min(missingLevel, 50), predictorMissing,
classMissing);
}
classifiers = Classifier.makeCopies(getClassifier(), 2);
evaluationB = new Evaluation(train);
evaluationI = new Evaluation(train);
classifiers[0].buildClassifier(train);
testWRTZeroR(classifiers[0], evaluationB, train, test);
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
classifiers[1].buildClassifier(new Instances(train, 0));
for (int i = 0; i < train.numInstances(); i++) {
((UpdateableClassifier)classifiers[1]).updateClassifier(
train.instance(i));
}
built = true;
testWRTZeroR(classifiers[1], evaluationI, train, test);
if (!evaluationB.equals(evaluationI)) {
println("no");
result[0] = false;
if (m_Debug) {
println("\n=== Full Report ===");
println("Results differ between batch and "
+ "incrementally built models.\n"
+ "Depending on the classifier, this may be OK");
println("Here are the results:\n");
println(evaluationB.toSummaryString(
"\nbatch built results\n", true));
println(evaluationI.toSummaryString(
"\nincrementally built results\n", true));
println("Here are the datasets:\n");
println("=== Train Dataset ===\n"
+ train.toString() + "\n");
println("=== Test Dataset ===\n"
+ test.toString() + "\n\n");
}
}
else {
println("yes");
result[0] = true;
}
} catch (Exception ex) {
result[0] = false;
print("Problem during");
if (built)
print(" testing");
else
print(" training");
println(": " + ex.getMessage() + "\n");
}
return result;
}
/**
* Checks whether the classifier erroneously uses the class
* value of test instances (if provided). Runs the classifier with
* test instance class values set to missing and compares with results
* when test instance class values are left intact.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @return index 0 is true if the test was passed
*/
protected boolean[] doesntUseTestClassVal(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
print("classifier ignores test instance class vals");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = 2*getNumInstances(), numTest = getNumInstances(),
numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
boolean[] result = new boolean[2];
Instances train = null;
Instances test = null;
Classifier [] classifiers = null;
boolean evalFail = false;
try {
train = makeTestDataset(42, numTrain,
nominalPredictor ? getNumNominal() + 1 : 0,
numericPredictor ? getNumNumeric() + 1 : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
test = makeTestDataset(24, numTest,
nominalPredictor ? getNumNominal() + 1 : 0,
numericPredictor ? getNumNumeric() + 1 : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
if (missingLevel > 0) {
addMissing(train, missingLevel, predictorMissing, classMissing);
addMissing(test, Math.min(missingLevel, 50), predictorMissing,
classMissing);
}
classifiers = Classifier.makeCopies(getClassifier(), 2);
classifiers[0].buildClassifier(train);
classifiers[1].buildClassifier(train);
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
// Now set test values to missing when predicting
for (int i = 0; i < test.numInstances(); i++) {
Instance testInst = test.instance(i);
Instance classMissingInst = (Instance)testInst.copy();
classMissingInst.setDataset(test);
classMissingInst.setClassMissing();
double [] dist0 = classifiers[0].distributionForInstance(testInst);
double [] dist1 = classifiers[1].distributionForInstance(classMissingInst);
for (int j = 0; j < dist0.length; j++) {
// ignore, if both are NaNs
if (Double.isNaN(dist0[j]) && Double.isNaN(dist1[j])) {
if (getDebug())
System.out.println("Both predictions are NaN!");
continue;
}
// distribution different?
if (dist0[j] != dist1[j]) {
throw new Exception("Prediction different for instance " + (i + 1));
}
}
}
println("yes");
result[0] = true;
} catch (Exception ex) {
println("no");
result[0] = false;
if (m_Debug) {
println("\n=== Full Report ===");
if (evalFail) {
println("Results differ between non-missing and "
+ "missing test class values.");
} else {
print("Problem during testing");
println(": " + ex.getMessage() + "\n");
}
println("Here are the datasets:\n");
println("=== Train Dataset ===\n"
+ train.toString() + "\n");
println("=== Train Weights ===\n");
for (int i = 0; i < train.numInstances(); i++) {
println(" " + (i + 1)
+ " " + train.instance(i).weight());
}
println("=== Test Dataset ===\n"
+ test.toString() + "\n\n");
println("(test weights all 1.0\n");
}
}
return result;
}
/**
* Checks whether the classifier can handle instance weights.
* This test compares the classifier performance on two datasets
* that are identical except for the training weights. If the
* results change, then the classifier must be using the weights. It
* may be possible to get a false positive from this test if the
* weight changes aren't significant enough to induce a change
* in classifier performance (but the weights are chosen to minimize
* the likelihood of this).
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @return index 0 true if the test was passed
*/
protected boolean[] instanceWeights(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
print("classifier uses instance weights");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = 2*getNumInstances(), numTest = getNumInstances(),
numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
boolean[] result = new boolean[2];
Instances train = null;
Instances test = null;
Classifier [] classifiers = null;
Evaluation evaluationB = null;
Evaluation evaluationI = null;
boolean built = false;
boolean evalFail = false;
try {
train = makeTestDataset(42, numTrain,
nominalPredictor ? getNumNominal() + 1 : 0,
numericPredictor ? getNumNumeric() + 1 : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
test = makeTestDataset(24, numTest,
nominalPredictor ? getNumNominal() + 1 : 0,
numericPredictor ? getNumNumeric() + 1 : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
if (missingLevel > 0) {
addMissing(train, missingLevel, predictorMissing, classMissing);
addMissing(test, Math.min(missingLevel, 50), predictorMissing,
classMissing);
}
classifiers = Classifier.makeCopies(getClassifier(), 2);
evaluationB = new Evaluation(train);
evaluationI = new Evaluation(train);
classifiers[0].buildClassifier(train);
testWRTZeroR(classifiers[0], evaluationB, train, test);
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
// Now modify instance weights and re-built/test
for (int i = 0; i < train.numInstances(); i++) {
train.instance(i).setWeight(0);
}
Random random = new Random(1);
for (int i = 0; i < train.numInstances() / 2; i++) {
int inst = Math.abs(random.nextInt()) % train.numInstances();
int weight = Math.abs(random.nextInt()) % 10 + 1;
train.instance(inst).setWeight(weight);
}
classifiers[1].buildClassifier(train);
built = true;
testWRTZeroR(classifiers[1], evaluationI, train, test);
if (evaluationB.equals(evaluationI)) {
// println("no");
evalFail = true;
throw new Exception("evalFail");
}
println("yes");
result[0] = true;
} catch (Exception ex) {
println("no");
result[0] = false;
if (m_Debug) {
println("\n=== Full Report ===");
if (evalFail) {
println("Results don't differ between non-weighted and "
+ "weighted instance models.");
println("Here are the results:\n");
println(evaluationB.toSummaryString("\nboth methods\n",
true));
} else {
print("Problem during");
if (built) {
print(" testing");
} else {
print(" training");
}
println(": " + ex.getMessage() + "\n");
}
println("Here are the datasets:\n");
println("=== Train Dataset ===\n"
+ train.toString() + "\n");
println("=== Train Weights ===\n");
for (int i = 0; i < train.numInstances(); i++) {
println(" " + (i + 1)
+ " " + train.instance(i).weight());
}
println("=== Test Dataset ===\n"
+ test.toString() + "\n\n");
println("(test weights all 1.0\n");
}
}
return result;
}
/**
* Checks whether the scheme alters the training dataset during
* training. If the scheme needs to modify the training
* data it should take a copy of the training data. Currently checks
* for changes to header structure, number of instances, order of
* instances, instance weights.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @param predictorMissing true if we know the classifier can handle
* (at least) moderate missing predictor values
* @param classMissing true if we know the classifier can handle
* (at least) moderate missing class values
* @return index 0 is true if the test was passed
*/
protected boolean[] datasetIntegrity(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
boolean predictorMissing,
boolean classMissing) {
print("classifier doesn't alter original datasets");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = getNumInstances(), numTest = getNumInstances(),
numClasses = 2, missingLevel = 20;
boolean[] result = new boolean[2];
Instances train = null;
Instances test = null;
Classifier classifier = null;
Evaluation evaluation = null;
boolean built = false;
try {
train = makeTestDataset(42, numTrain,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
test = makeTestDataset(24, numTest,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
if (missingLevel > 0) {
addMissing(train, missingLevel, predictorMissing, classMissing);
addMissing(test, Math.min(missingLevel, 50), predictorMissing,
classMissing);
}
classifier = Classifier.makeCopies(getClassifier(), 1)[0];
evaluation = new Evaluation(train);
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
Instances trainCopy = new Instances(train);
Instances testCopy = new Instances(test);
classifier.buildClassifier(trainCopy);
compareDatasets(train, trainCopy);
built = true;
testWRTZeroR(classifier, evaluation, trainCopy, testCopy);
compareDatasets(test, testCopy);
println("yes");
result[0] = true;
} catch (Exception ex) {
println("no");
result[0] = false;
if (m_Debug) {
println("\n=== Full Report ===");
print("Problem during");
if (built) {
print(" testing");
} else {
print(" training");
}
println(": " + ex.getMessage() + "\n");
println("Here are the datasets:\n");
println("=== Train Dataset ===\n"
+ train.toString() + "\n");
println("=== Test Dataset ===\n"
+ test.toString() + "\n\n");
}
}
return result;
}
/**
* Runs a text on the datasets with the given characteristics.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @param missingLevel the percentage of missing values
* @param predictorMissing true if the missing values may be in
* the predictors
* @param classMissing true if the missing values may be in the class
* @param numTrain the number of instances in the training set
* @param numTest the number of instaces in the test set
* @param numClasses the number of classes
* @param accepts the acceptable string in an exception
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
*/
protected boolean[] runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numTest,
int numClasses,
FastVector accepts) {
return runBasicTest(
nominalPredictor,
numericPredictor,
stringPredictor,
datePredictor,
relationalPredictor,
multiInstance,
classType,
TestInstances.CLASS_IS_LAST,
missingLevel,
predictorMissing,
classMissing,
numTrain,
numTest,
numClasses,
accepts);
}
/**
* Runs a text on the datasets with the given characteristics.
*
* @param nominalPredictor if true use nominal predictor attributes
* @param numericPredictor if true use numeric predictor attributes
* @param stringPredictor if true use string predictor attributes
* @param datePredictor if true use date predictor attributes
* @param relationalPredictor if true use relational predictor attributes
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @param classIndex the attribute index of the class
* @param missingLevel the percentage of missing values
* @param predictorMissing true if the missing values may be in
* the predictors
* @param classMissing true if the missing values may be in the class
* @param numTrain the number of instances in the training set
* @param numTest the number of instaces in the test set
* @param numClasses the number of classes
* @param accepts the acceptable string in an exception
* @return index 0 is true if the test was passed, index 1 is true if test
* was acceptable
*/
protected boolean[] runBasicTest(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType,
int classIndex,
int missingLevel,
boolean predictorMissing,
boolean classMissing,
int numTrain,
int numTest,
int numClasses,
FastVector accepts) {
boolean[] result = new boolean[2];
Instances train = null;
Instances test = null;
Classifier classifier = null;
Evaluation evaluation = null;
boolean built = false;
try {
train = makeTestDataset(42, numTrain,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
classIndex,
multiInstance);
test = makeTestDataset(24, numTest,
nominalPredictor ? getNumNominal() : 0,
numericPredictor ? getNumNumeric() : 0,
stringPredictor ? getNumString() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
classIndex,
multiInstance);
if (missingLevel > 0) {
addMissing(train, missingLevel, predictorMissing, classMissing);
addMissing(test, Math.min(missingLevel, 50), predictorMissing,
classMissing);
}
classifier = Classifier.makeCopies(getClassifier(), 1)[0];
evaluation = new Evaluation(train);
} catch (Exception ex) {
ex.printStackTrace();
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
classifier.buildClassifier(train);
built = true;
if (!testWRTZeroR(classifier, evaluation, train, test)[0]) {
result[0] = true;
result[1] = true;
throw new Exception("Scheme performs worse than ZeroR");
}
println("yes");
result[0] = true;
}
catch (Exception ex) {
boolean acceptable = false;
String msg;
if (ex.getMessage() == null)
msg = "";
else
msg = ex.getMessage().toLowerCase();
if (msg.indexOf("not in classpath") > -1)
m_ClasspathProblems = true;
if (msg.indexOf("worse than zeror") >= 0) {
println("warning: performs worse than ZeroR");
result[0] = true;
result[1] = true;
} else {
for (int i = 0; i < accepts.size(); i++) {
if (msg.indexOf((String)accepts.elementAt(i)) >= 0) {
acceptable = true;
}
}
println("no" + (acceptable ? " (OK error message)" : ""));
result[1] = acceptable;
}
if (m_Debug) {
println("\n=== Full Report ===");
print("Problem during");
if (built) {
print(" testing");
} else {
print(" training");
}
println(": " + ex.getMessage() + "\n");
if (!acceptable) {
if (accepts.size() > 0) {
print("Error message doesn't mention ");
for (int i = 0; i < accepts.size(); i++) {
if (i != 0) {
print(" or ");
}
print('"' + (String)accepts.elementAt(i) + '"');
}
}
println("here are the datasets:\n");
println("=== Train Dataset ===\n"
+ train.toString() + "\n");
println("=== Test Dataset ===\n"
+ test.toString() + "\n\n");
}
}
}
return result;
}
/**
* Determine whether the scheme performs worse than ZeroR during testing
*
* @param classifier the pre-trained classifier
* @param evaluation the classifier evaluation object
* @param train the training data
* @param test the test data
* @return index 0 is true if the scheme performs better than ZeroR
* @throws Exception if there was a problem during the scheme's testing
*/
protected boolean[] testWRTZeroR(Classifier classifier,
Evaluation evaluation,
Instances train, Instances test)
throws Exception {
boolean[] result = new boolean[2];
evaluation.evaluateModel(classifier, test);
try {
// Tested OK, compare with ZeroR
Classifier zeroR = new weka.classifiers.rules.ZeroR();
zeroR.buildClassifier(train);
Evaluation zeroREval = new Evaluation(train);
zeroREval.evaluateModel(zeroR, test);
result[0] = Utils.grOrEq(zeroREval.errorRate(), evaluation.errorRate());
}
catch (Exception ex) {
throw new Error("Problem determining ZeroR performance: "
+ ex.getMessage());
}
return result;
}
/**
* Make a simple set of instances, which can later be modified
* for use in specific tests.
*
* @param seed the random number seed
* @param numInstances the number of instances to generate
* @param numNominal the number of nominal attributes
* @param numNumeric the number of numeric attributes
* @param numString the number of string attributes
* @param numDate the number of date attributes
* @param numRelational the number of relational attributes
* @param numClasses the number of classes (if nominal class)
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @param multiInstance whether the dataset should a multi-instance dataset
* @return the test dataset
* @throws Exception if the dataset couldn't be generated
* @see #process(Instances)
*/
protected Instances makeTestDataset(int seed, int numInstances,
int numNominal, int numNumeric,
int numString, int numDate,
int numRelational,
int numClasses, int classType,
boolean multiInstance)
throws Exception {
return makeTestDataset(
seed,
numInstances,
numNominal,
numNumeric,
numString,
numDate,
numRelational,
numClasses,
classType,
TestInstances.CLASS_IS_LAST,
multiInstance);
}
/**
* Make a simple set of instances with variable position of the class
* attribute, which can later be modified for use in specific tests.
*
* @param seed the random number seed
* @param numInstances the number of instances to generate
* @param numNominal the number of nominal attributes
* @param numNumeric the number of numeric attributes
* @param numString the number of string attributes
* @param numDate the number of date attributes
* @param numRelational the number of relational attributes
* @param numClasses the number of classes (if nominal class)
* @param classType the class type (NUMERIC, NOMINAL, etc.)
* @param classIndex the index of the class (0-based, -1 as last)
* @param multiInstance whether the dataset should a multi-instance dataset
* @return the test dataset
* @throws Exception if the dataset couldn't be generated
* @see TestInstances#CLASS_IS_LAST
* @see #process(Instances)
*/
protected Instances makeTestDataset(int seed, int numInstances,
int numNominal, int numNumeric,
int numString, int numDate,
int numRelational,
int numClasses, int classType,
int classIndex,
boolean multiInstance)
throws Exception {
TestInstances dataset = new TestInstances();
dataset.setSeed(seed);
dataset.setNumInstances(numInstances);
dataset.setNumNominal(numNominal);
dataset.setNumNumeric(numNumeric);
dataset.setNumString(numString);
dataset.setNumDate(numDate);
dataset.setNumRelational(numRelational);
dataset.setNumClasses(numClasses);
dataset.setClassType(classType);
dataset.setClassIndex(classIndex);
dataset.setNumClasses(numClasses);
dataset.setMultiInstance(multiInstance);
dataset.setWords(getWords());
dataset.setWordSeparators(getWordSeparators());
return process(dataset.generate());
}
/**
* Print out a short summary string for the dataset characteristics
*
* @param nominalPredictor true if nominal predictor attributes are present
* @param numericPredictor true if numeric predictor attributes are present
* @param stringPredictor true if string predictor attributes are present
* @param datePredictor true if date predictor attributes are present
* @param relationalPredictor true if relational predictor attributes are present
* @param multiInstance whether multi-instance is needed
* @param classType the class type (NUMERIC, NOMINAL, etc.)
*/
protected void printAttributeSummary(boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
String str = "";
if (numericPredictor)
str += " numeric";
if (nominalPredictor) {
if (str.length() > 0)
str += " &";
str += " nominal";
}
if (stringPredictor) {
if (str.length() > 0)
str += " &";
str += " string";
}
if (datePredictor) {
if (str.length() > 0)
str += " &";
str += " date";
}
if (relationalPredictor) {
if (str.length() > 0)
str += " &";
str += " relational";
}
str += " predictors)";
switch (classType) {
case Attribute.NUMERIC:
str = " (numeric class," + str;
break;
case Attribute.NOMINAL:
str = " (nominal class," + str;
break;
case Attribute.STRING:
str = " (string class," + str;
break;
case Attribute.DATE:
str = " (date class," + str;
break;
case Attribute.RELATIONAL:
str = " (relational class," + str;
break;
}
print(str);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.33 $");
}
/**
* Test method for this class
*
* @param args the commandline parameters
*/
public static void main(String [] args) {
runCheck(new CheckClassifier(), args);
}
}
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