weka.associations.CheckAssociator Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of weka-stable Show documentation
Show all versions of weka-stable Show documentation
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.
*/
/*
* CheckAssociator.java
* Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
*
*/
package weka.associations;
import weka.core.Attribute;
import weka.core.CheckScheme;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
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
* associators. If you implement an associators 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 associators don't exist, but this will help find some
* common ones.
*
* Typical usage:
* java weka.associations.CheckAssociator -W associator_name
* -- associator_options
*
* CheckAssociator reports on the following:
*
* - Associator abilities
*
* - Possible command line options to the associators
* - Whether the associators can predict nominal, numeric, string,
* date or relational class attributes.
* - Whether the associators can handle numeric predictor attributes
* - Whether the associators can handle nominal predictor attributes
* - Whether the associators can handle string predictor attributes
* - Whether the associators can handle date predictor attributes
* - Whether the associators can handle relational predictor attributes
* - Whether the associators can handle multi-instance data
* - Whether the associators can handle missing predictor values
* - Whether the associators can handle missing class values
* - Whether a nominal associators only handles 2 class problems
* - Whether the associators can handle instance weights
*
*
* - Correct functioning
*
* - Correct initialisation during buildAssociations (i.e. no result
* changes when buildAssociations called repeatedly)
* - Whether the associators alters the data pased to it
* (number of instances, instance order, instance weights, etc)
*
*
* - Degenerate cases
*
* - building associators 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 CheckAssociator with the debug option set will output the
* training dataset for any failed tests.
*
* The weka.associations.AbstractAssociatorTest uses this
* class to test all the associators. 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 associator analysed.
* eg: weka.associations.Apriori
* (default weka.associations.Apriori)
*
*
* Options specific to associator weka.associations.Apriori:
*
*
* -N <required number of rules output>
* The required number of rules. (default = 10)
*
* -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
* The metric type by which to rank rules. (default = confidence)
*
* -C <minimum metric score of a rule>
* The minimum confidence of a rule. (default = 0.9)
*
* -D <delta for minimum support>
* The delta by which the minimum support is decreased in
* each iteration. (default = 0.05)
*
* -U <upper bound for minimum support>
* Upper bound for minimum support. (default = 1.0)
*
* -M <lower bound for minimum support>
* The lower bound for the minimum support. (default = 0.1)
*
* -S <significance level>
* If used, rules are tested for significance at
* the given level. Slower. (default = no significance testing)
*
* -I
* If set the itemsets found are also output. (default = no)
*
* -R
* Remove columns that contain all missing values (default = no)
*
* -V
* Report progress iteratively. (default = no)
*
* -A
* If set class association rules are mined. (default = no)
*
* -c <the class index>
* The class index. (default = last)
*
*
* Options after -- are passed to the designated associator.
*
* @author Len Trigg ([email protected])
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 1.7 $
* @see TestInstances
*/
public class CheckAssociator
extends CheckScheme
implements RevisionHandler {
/*
* Note about test methods:
* - methods return array of booleans
* - first index: success or not
* - second index: acceptable or not (e.g., Exception is OK)
*
* FracPete (fracpete at waikato dot ac dot nz)
*/
/** a "dummy" class type */
public final static int NO_CLASS = -1;
/*** The associator to be examined */
protected Associator m_Associator = new weka.associations.Apriori();
/**
* 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 associator analysed.\n"
+"\teg: weka.associations.Apriori\n"
+ "\t(default weka.associations.Apriori)",
"W", 1, "-W"));
if ((m_Associator != null)
&& (m_Associator instanceof OptionHandler)) {
result.addElement(new Option("", "", 0,
"\nOptions specific to associator "
+ m_Associator.getClass().getName()
+ ":"));
Enumeration enu = ((OptionHandler)m_Associator).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 associator analysed.
* eg: weka.associations.Apriori
* (default weka.associations.Apriori)
*
*
* Options specific to associator weka.associations.Apriori:
*
*
* -N <required number of rules output>
* The required number of rules. (default = 10)
*
* -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
* The metric type by which to rank rules. (default = confidence)
*
* -C <minimum metric score of a rule>
* The minimum confidence of a rule. (default = 0.9)
*
* -D <delta for minimum support>
* The delta by which the minimum support is decreased in
* each iteration. (default = 0.05)
*
* -U <upper bound for minimum support>
* Upper bound for minimum support. (default = 1.0)
*
* -M <lower bound for minimum support>
* The lower bound for the minimum support. (default = 0.1)
*
* -S <significance level>
* If used, rules are tested for significance at
* the given level. Slower. (default = no significance testing)
*
* -I
* If set the itemsets found are also output. (default = no)
*
* -R
* Remove columns that contain all missing values (default = no)
*
* -V
* Report progress iteratively. (default = no)
*
* -A
* If set class association rules are mined. (default = no)
*
* -c <the class index>
* The class index. (default = last)
*
*
* @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.associations.Apriori.class.getName();
setAssociator(
(Associator) forName(
"weka.associations",
Associator.class,
tmpStr,
Utils.partitionOptions(options)));
}
/**
* Gets the current settings of the CheckAssociator.
*
* @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 (getAssociator() != null) {
result.add("-W");
result.add(getAssociator().getClass().getName());
}
if ((m_Associator != null) && (m_Associator instanceof OptionHandler))
options = ((OptionHandler) m_Associator).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 (getAssociator() == null) {
println("\n=== No associator set ===");
return;
}
println("\n=== Check on Associator: "
+ getAssociator().getClass().getName()
+ " ===\n");
// Start tests
m_ClasspathProblems = false;
println("--> Checking for interfaces");
canTakeOptions();
boolean weightedInstancesHandler = weightedInstancesHandler()[0];
boolean multiInstanceHandler = multiInstanceHandler()[0];
println("--> Associator tests");
declaresSerialVersionUID();
println("--> no class attribute");
testsWithoutClass(weightedInstancesHandler, multiInstanceHandler);
println("--> with class attribute");
testsPerClassType(Attribute.NOMINAL, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.NUMERIC, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.DATE, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.STRING, weightedInstancesHandler, multiInstanceHandler);
testsPerClassType(Attribute.RELATIONAL, weightedInstancesHandler, multiInstanceHandler);
}
/**
* Set the associator to test.
*
* @param newAssociator the Associator to use.
*/
public void setAssociator(Associator newAssociator) {
m_Associator = newAssociator;
}
/**
* Get the associator being tested
*
* @return the associator being tested
*/
public Associator getAssociator() {
return m_Associator;
}
/**
* Run a battery of tests for a given class attribute type
*
* @param classType true if the class attribute should be numeric
* @param weighted true if the associator says it handles weights
* @param multiInstance true if the associator is a multi-instance associator
*/
protected void testsPerClassType(int classType,
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);
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);
}
}
/**
* Run a battery of tests without a class
*
* @param weighted true if the associator says it handles weights
* @param multiInstance true if the associator is a multi-instance associator
*/
protected void testsWithoutClass(boolean weighted,
boolean multiInstance) {
boolean PNom = canPredict(true, false, false, false, false, multiInstance, NO_CLASS)[0];
boolean PNum = canPredict(false, true, false, false, false, multiInstance, NO_CLASS)[0];
boolean PStr = canPredict(false, false, true, false, false, multiInstance, NO_CLASS)[0];
boolean PDat = canPredict(false, false, false, true, false, multiInstance, NO_CLASS)[0];
boolean PRel;
if (!multiInstance)
PRel = canPredict(false, false, false, false, true, multiInstance, NO_CLASS)[0];
else
PRel = false;
if (PNom || PNum || PStr || PDat || PRel) {
if (weighted)
instanceWeights(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS);
canHandleZeroTraining(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS);
boolean handleMissingPredictors = canHandleMissing(PNom, PNum, PStr, PDat, PRel,
multiInstance, NO_CLASS,
true, false, 20)[0];
if (handleMissingPredictors)
canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS, true, false, 100);
correctBuildInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS);
datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS,
handleMissingPredictors, false);
}
}
/**
* Checks whether the scheme can take command line options.
*
* @return index 0 is true if the associator can take options
*/
protected boolean[] canTakeOptions() {
boolean[] result = new boolean[2];
print("options...");
if (m_Associator instanceof OptionHandler) {
println("yes");
if (m_Debug) {
println("\n=== Full report ===");
Enumeration enu = ((OptionHandler)m_Associator).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 says it can handle instance weights.
*
* @return true if the associator handles instance weights
*/
protected boolean[] weightedInstancesHandler() {
boolean[] result = new boolean[2];
print("weighted instances associator...");
if (m_Associator 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 associator handles multi-instance data
*/
protected boolean[] multiInstanceHandler() {
boolean[] result = new boolean[2];
print("multi-instance associator...");
if (m_Associator instanceof MultiInstanceCapabilitiesHandler) {
println("yes");
result[0] = true;
}
else {
println("no");
result[0] = false;
}
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_Associator.getClass());
if (result[0])
println("yes");
else
println("no");
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("any");
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(), numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numClasses,
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(), missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
Attribute.NOMINAL,
missingLevel, predictorMissing, classMissing,
numTrain, 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(), numClasses = 2,
missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
classIndex,
missingLevel, predictorMissing, classMissing,
numTrain, 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, numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
return runBasicTest(
nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numClasses,
accepts);
}
/**
* Checks whether the scheme correctly initialises models when
* buildAssociations is called. This test calls buildAssociations with
* one training dataset. buildAssociations is then called on a training
* set with different structure, and then again with the original training
* set. If the equals method of the AssociatorEvaluation class returns
* false, this is 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
*/
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 buildAssociations");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = getNumInstances(),
numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
Instances train1 = null;
Instances train2 = null;
Associator associator = null;
AssociatorEvaluation evaluation1A = null;
AssociatorEvaluation evaluation1B = null;
AssociatorEvaluation evaluation2 = null;
int stage = 0;
try {
// Make two train sets 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() + 1 : 0,
datePredictor ? getNumDate() + 1 : 0,
relationalPredictor ? getNumRelational() + 1 : 0,
numClasses,
classType,
multiInstance);
if (missingLevel > 0) {
addMissing(train1, missingLevel, predictorMissing, classMissing);
addMissing(train2, missingLevel, predictorMissing, classMissing);
}
associator = AbstractAssociator.makeCopies(getAssociator(), 1)[0];
evaluation1A = new AssociatorEvaluation();
evaluation1B = new AssociatorEvaluation();
evaluation2 = new AssociatorEvaluation();
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
stage = 0;
evaluation1A.evaluate(associator, train1);
stage = 1;
evaluation2.evaluate(associator, train2);
stage = 2;
evaluation1B.evaluate(associator, train1);
stage = 3;
if (!evaluation1A.equals(evaluation1B)) {
if (m_Debug) {
println("\n=== Full report ===\n"
+ evaluation1A.toSummaryString("\nFirst buildAssociations()")
+ "\n\n");
println(
evaluation1B.toSummaryString("\nSecond buildAssociations()")
+ "\n\n");
}
throw new Exception("Results differ between buildAssociations calls");
}
println("yes");
result[0] = true;
if (false && m_Debug) {
println("\n=== Full report ===\n"
+ evaluation1A.toSummaryString("\nFirst buildAssociations()")
+ "\n\n");
println(
evaluation1B.toSummaryString("\nSecond buildAssociations()")
+ "\n\n");
}
}
catch (Exception ex) {
println("no");
result[0] = false;
if (m_Debug) {
println("\n=== Full Report ===");
print("Problem during building");
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("=== Train2 Dataset ===\n"
+ train2.toString() + "\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(), numClasses = 2;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numClasses,
accepts);
}
/**
* Checks whether the associator can handle instance weights.
* This test compares the associator performance on two datasets
* that are identical except for the training weights. If the
* results change, then the associator 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 associator 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("associator uses instance weights");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = 2*getNumInstances(),
numClasses = 2, missingLevel = 0;
boolean predictorMissing = false, classMissing = false;
boolean[] result = new boolean[2];
Instances train = null;
Associator [] associators = null;
AssociatorEvaluation evaluationB = null;
AssociatorEvaluation evaluationI = 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);
if (missingLevel > 0)
addMissing(train, missingLevel, predictorMissing, classMissing);
associators = AbstractAssociator.makeCopies(getAssociator(), 2);
evaluationB = new AssociatorEvaluation();
evaluationI = new AssociatorEvaluation();
evaluationB.evaluate(associators[0], train);
} 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);
}
evaluationI.evaluate(associators[1], train);
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"));
} else {
print("Problem during building");
println(": " + ex.getMessage() + "\n");
}
println("Here is the dataset:\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());
}
}
}
return result;
}
/**
* Checks whether the scheme alters the training dataset during
* building. If the scheme needs to modify the 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 associator can handle
* (at least) moderate missing predictor values
* @param classMissing true if we know the associator 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("associator doesn't alter original datasets");
printAttributeSummary(
nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType);
print("...");
int numTrain = getNumInstances(),
numClasses = 2, missingLevel = 20;
boolean[] result = new boolean[2];
Instances train = null;
Associator associator = null;
try {
train = makeTestDataset(42, numTrain,
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);
associator = AbstractAssociator.makeCopies(getAssociator(), 1)[0];
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
Instances trainCopy = new Instances(train);
associator.buildAssociations(trainCopy);
compareDatasets(train, trainCopy);
println("yes");
result[0] = true;
} catch (Exception ex) {
println("no");
result[0] = false;
if (m_Debug) {
println("\n=== Full Report ===");
print("Problem during building");
println(": " + ex.getMessage() + "\n");
println("Here is the dataset:\n");
println("=== Train Dataset ===\n"
+ train.toString() + "\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 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 numClasses,
FastVector accepts) {
return runBasicTest(
nominalPredictor,
numericPredictor,
stringPredictor,
datePredictor,
relationalPredictor,
multiInstance,
classType,
TestInstances.CLASS_IS_LAST,
missingLevel,
predictorMissing,
classMissing,
numTrain,
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 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 numClasses,
FastVector accepts) {
boolean[] result = new boolean[2];
Instances train = null;
Associator associator = null;
try {
train = makeTestDataset(42, numTrain,
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);
associator = AbstractAssociator.makeCopies(getAssociator(), 1)[0];
} catch (Exception ex) {
ex.printStackTrace();
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
associator.buildAssociations(train);
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;
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 building");
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 is the dataset:\n");
println("=== Train Dataset ===\n"
+ train.toString() + "\n");
}
}
}
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);
if (classType == NO_CLASS) {
dataset.setClassType(Attribute.NOMINAL); // ignored
dataset.setClassIndex(TestInstances.NO_CLASS);
}
else {
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;
case NO_CLASS:
str = " (no class," + str;
break;
}
print(str);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.7 $");
}
/**
* Test method for this class
*
* @param args the commandline parameters
*/
public static void main(String [] args) {
runCheck(new CheckAssociator(), args);
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy