CheckAttributeSelection.java
| Index Score | ||
|---|---|---|
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weka.attributeSelection |
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Weka |
View: Reasons, Metrics, Source Code
These are the metrics that contribute to the Enerjy Score for this file, ranked by impact. So the metrics listed at the top influence the score to a greater extent that the metrics listed at the bottom.
| Metric | Description | |
|---|---|---|
| DOC_COMMENT | Number of javadoc comment lines |  |
| SIZE | Size of the file in bytes | |
| PARAMS | Number of formal parameter declarations | |
| JAVA0034 | JAVA0034 Missing braces in if statement | |
| COMMENTS | Comment lines | |
| ELOC | Effective lines of code | |
| LINES | Number of lines in the source file | |
| LOC | Lines of code | |
| LOGICAL_LINES | Number of statements | |
| INTERFACE_COMPLEXITY | Interface complexity | |
| PROGRAM_LENGTH | Halstead program length | |
| OPERANDS | Number of operands | |
| OPERATORS | Number of operators | |
| UNIQUE_OPERANDS | Number of unique operands | |
| EXITS | Procedure exits | |
| CYCLOMATIC | Cyclomatic complexity | |
| PROGRAM_VOCAB | Halstead program vocabulary | |
| DECL_COMMENTS | Comments in declarations | |
| COMPARISONS | Number of comparison operators | |
| JAVA0177 | JAVA0177 Variable declaration missing initializer | |
| JAVA0150 | JAVA0150 java.lang.Error (or subclass) thrown | |
| JAVA0138 | JAVA0138 N parameters defined for method (maximum: M) | |
| JAVA0166 | JAVA0166 Generic exception caught | |
| LOOPS | Number of loops | |
| JAVA0036 | JAVA0036 Missing braces in while statement | |
| BLOCKS | Number of blocks | |
| JAVA0076 | JAVA0076 Use of magic number | |
| RETURNS | Number of return points from functions | |
| JAVA0032 | JAVA0032 Switch statement missing default | |
| FUNCTIONS | Number of function declarations | |
| WHITESPACE | Number of whitespace lines | |
| NEST_DEPTH | Maximum nesting depth | |
| UNIQUE_OPERATORS | Number of unique operators | |
| JAVA0117 | JAVA0117 Missing javadoc: method 'method' | |
| PROGRAM_VOLUME | Halstead program volume | |
| JAVA0136 | JAVA0136 N methods defined in class (maximum: M) | |
| JAVA0035 | JAVA0035 Missing braces in for statement | |
| JAVA0126 | JAVA0126 Method declares unchecked exception in throws | |
| JAVA0110 | JAVA0110 Incorrect javadoc: no @return tag | |
| LINE_COMMENT | Number of line comments | |
| JAVA0265 | JAVA0265 Use of Throwable.printStackTrace() | |
| JAVA0173 | JAVA0173 Unused method parameter | |
| EXEC_COMMENTS | Comments in executable code | |
| JAVA0108 | JAVA0108 Incorrect javadoc: no @param tag for 'parameter' | |
| JAVA0145 | JAVA0145 Tab character used in source file | |
/*
* 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.
*/
/*
* CheckAttributeSelection.java
* Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
*
*/
package weka.attributeSelection;
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.RevisionUtils;
import weka.core.SerializationHelper;
import weka.core.SerializedObject;
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
* attribute selection schemes. If you implement an attribute selection 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 attribute selection don't exist, but this will help find some
* common ones. <p/>
*
* Typical usage: <p/>
* <code>java weka.attributeSelection.CheckAttributeSelection -W ASscheme_name
* -- ASscheme_options </code><p/>
*
* CheckAttributeSelection reports on the following:
* <ul>
* <li> Scheme abilities
* <ul>
* <li> Possible command line options to the scheme </li>
* <li> Whether the scheme can predict nominal, numeric, string,
* date or relational class attributes. </li>
* <li> Whether the scheme can handle numeric predictor attributes </li>
* <li> Whether the scheme can handle nominal predictor attributes </li>
* <li> Whether the scheme can handle string predictor attributes </li>
* <li> Whether the scheme can handle date predictor attributes </li>
* <li> Whether the scheme can handle relational predictor attributes </li>
* <li> Whether the scheme can handle multi-instance data </li>
* <li> Whether the scheme can handle missing predictor values </li>
* <li> Whether the scheme can handle missing class values </li>
* <li> Whether a nominal scheme only handles 2 class problems </li>
* <li> Whether the scheme can handle instance weights </li>
* </ul>
* </li>
* <li> Correct functioning
* <ul>
* <li> Correct initialisation during search (i.e. no result
* changes when search is performed repeatedly) </li>
* <li> Whether the scheme alters the data pased to it
* (number of instances, instance order, instance weights, etc) </li>
* </ul>
* </li>
* <li> Degenerate cases
* <ul>
* <li> building scheme with zero instances </li>
* <li> all but one predictor attribute values missing </li>
* <li> all predictor attribute values missing </li>
* <li> all but one class values missing </li>
* <li> all class values missing </li>
* </ul>
* </li>
* </ul>
* Running CheckAttributeSelection with the debug option set will output the
* training dataset for any failed tests.<p/>
*
* The <code>weka.attributeSelection.AbstractAttributeSelectionTest</code>
* uses this class to test all the schemes. Any changes here, have to be
* checked in that abstract test class, too. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -S
* Silent mode - prints nothing to stdout.</pre>
*
* <pre> -N <num>
* The number of instances in the datasets (default 20).</pre>
*
* <pre> -nominal <num>
* The number of nominal attributes (default 2).</pre>
*
* <pre> -nominal-values <num>
* The number of values for nominal attributes (default 1).</pre>
*
* <pre> -numeric <num>
* The number of numeric attributes (default 1).</pre>
*
* <pre> -string <num>
* The number of string attributes (default 1).</pre>
*
* <pre> -date <num>
* The number of date attributes (default 1).</pre>
*
* <pre> -relational <num>
* The number of relational attributes (default 1).</pre>
*
* <pre> -num-instances-relational <num>
* The number of instances in relational/bag attributes (default 10).</pre>
*
* <pre> -words <comma-separated-list>
* The words to use in string attributes.</pre>
*
* <pre> -word-separators <chars>
* The word separators to use in string attributes.</pre>
*
* <pre> -eval name [options]
* Full name and options of the evaluator analyzed.
* eg: weka.attributeSelection.CfsSubsetEval</pre>
*
* <pre> -search name [options]
* Full name and options of the search method analyzed.
* eg: weka.attributeSelection.Ranker</pre>
*
* <pre> -test <eval|search>
* The scheme to test, either the evaluator or the search method.
* (Default: eval)</pre>
*
* <pre>
* Options specific to evaluator weka.attributeSelection.CfsSubsetEval:
* </pre>
*
* <pre> -M
* Treat missing values as a seperate value.</pre>
*
* <pre> -L
* Don't include locally predictive attributes.</pre>
*
* <pre>
* Options specific to search method weka.attributeSelection.Ranker:
* </pre>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.
* Any starting attributes specified are
* ignored during the ranking.</pre>
*
* <pre> -T <threshold>
* Specify a theshold by which attributes
* may be discarded from the ranking.</pre>
*
* <pre> -N <num to select>
* Specify number of attributes to select</pre>
*
<!-- options-end -->
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 1.7 $
* @see TestInstances
*/
public class CheckAttributeSelection
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)
*
* FracPete (fracpete at waikato dot ac dot nz)
*/
/*** The evaluator to be examined */
protected ASEvaluation m_Evaluator = new CfsSubsetEval();
/*** The search method to be used */
protected ASSearch m_Search = new Ranker();
/** whether to test the evaluator (default) or the search method */
protected boolean m_TestEvaluator = true;
/**
* 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 and options of the evaluator analyzed.\n"
+"\teg: weka.attributeSelection.CfsSubsetEval",
"eval", 1, "-eval name [options]"));
result.addElement(new Option(
"\tFull name and options of the search method analyzed.\n"
+"\teg: weka.attributeSelection.Ranker",
"search", 1, "-search name [options]"));
result.addElement(new Option(
"\tThe scheme to test, either the evaluator or the search method.\n"
+"\t(Default: eval)",
"test", 1, "-test <eval|search>"));
if ((m_Evaluator != null) && (m_Evaluator instanceof OptionHandler)) {
result.addElement(new Option("", "", 0,
"\nOptions specific to evaluator "
+ m_Evaluator.getClass().getName()
+ ":"));
Enumeration enm = ((OptionHandler) m_Evaluator).listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
}
if ((m_Search != null) && (m_Search instanceof OptionHandler)) {
result.addElement(new Option("", "", 0,
"\nOptions specific to search method "
+ m_Search.getClass().getName()
+ ":"));
Enumeration enm = ((OptionHandler) m_Search).listOptions();
while (enm.hasMoreElements())
result.addElement(enm.nextElement());
}
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* Turn on debugging output.</pre>
*
* <pre> -S
* Silent mode - prints nothing to stdout.</pre>
*
* <pre> -N <num>
* The number of instances in the datasets (default 20).</pre>
*
* <pre> -nominal <num>
* The number of nominal attributes (default 2).</pre>
*
* <pre> -nominal-values <num>
* The number of values for nominal attributes (default 1).</pre>
*
* <pre> -numeric <num>
* The number of numeric attributes (default 1).</pre>
*
* <pre> -string <num>
* The number of string attributes (default 1).</pre>
*
* <pre> -date <num>
* The number of date attributes (default 1).</pre>
*
* <pre> -relational <num>
* The number of relational attributes (default 1).</pre>
*
* <pre> -num-instances-relational <num>
* The number of instances in relational/bag attributes (default 10).</pre>
*
* <pre> -words <comma-separated-list>
* The words to use in string attributes.</pre>
*
* <pre> -word-separators <chars>
* The word separators to use in string attributes.</pre>
*
* <pre> -eval name [options]
* Full name and options of the evaluator analyzed.
* eg: weka.attributeSelection.CfsSubsetEval</pre>
*
* <pre> -search name [options]
* Full name and options of the search method analyzed.
* eg: weka.attributeSelection.Ranker</pre>
*
* <pre> -test <eval|search>
* The scheme to test, either the evaluator or the search method.
* (Default: eval)</pre>
*
* <pre>
* Options specific to evaluator weka.attributeSelection.CfsSubsetEval:
* </pre>
*
* <pre> -M
* Treat missing values as a seperate value.</pre>
*
* <pre> -L
* Don't include locally predictive attributes.</pre>
*
* <pre>
* Options specific to search method weka.attributeSelection.Ranker:
* </pre>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.
* Any starting attributes specified are
* ignored during the ranking.</pre>
*
* <pre> -T <threshold>
* Specify a theshold by which attributes
* may be discarded from the ranking.</pre>
*
* <pre> -N <num to select>
* Specify number of attributes to select</pre>
*
<!-- options-end -->
*
* @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;
String[] tmpOptions;
super.setOptions(options);
tmpStr = Utils.getOption("eval", options);
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length != 0) {
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setEvaluator(
(ASEvaluation) forName(
"weka.attributeSelection",
ASEvaluation.class,
tmpStr,
tmpOptions));
}
tmpStr = Utils.getOption("search", options);
tmpOptions = Utils.splitOptions(tmpStr);
if (tmpOptions.length != 0) {
tmpStr = tmpOptions[0];
tmpOptions[0] = "";
setSearch(
(ASSearch) forName(
"weka.attributeSelection",
ASSearch.class,
tmpStr,
tmpOptions));
}
tmpStr = Utils.getOption("test", options);
setTestEvaluator(!tmpStr.equalsIgnoreCase("search"));
}
/**
* Gets the current settings of the CheckAttributeSelection.
*
* @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]);
result.add("-eval");
if (getEvaluator() instanceof OptionHandler)
result.add(
getEvaluator().getClass().getName()
+ " "
+ Utils.joinOptions(((OptionHandler) getEvaluator()).getOptions()));
else
result.add(
getEvaluator().getClass().getName());
result.add("-search");
if (getSearch() instanceof OptionHandler)
result.add(
getSearch().getClass().getName()
+ " "
+ Utils.joinOptions(((OptionHandler) getSearch()).getOptions()));
else
result.add(
getSearch().getClass().getName());
result.add("-test");
if (getTestEvaluator())
result.add("eval");
else
result.add("search");
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Begin the tests, reporting results to System.out
*/
public void doTests() {
if (getTestObject() == null) {
println("\n=== No scheme set ===");
return;
}
println("\n=== Check on scheme: "
+ getTestObject().getClass().getName()
+ " ===\n");
// Start tests
m_ClasspathProblems = false;
println("--> Checking for interfaces");
canTakeOptions();
boolean weightedInstancesHandler = weightedInstancesHandler()[0];
boolean multiInstanceHandler = multiInstanceHandler()[0];
println("--> Scheme tests");
declaresSerialVersionUID();
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 evaluator to test.
*
* @param value the evaluator to use.
*/
public void setEvaluator(ASEvaluation value) {
m_Evaluator = value;
}
/**
* Get the current evaluator
*
* @return the current evaluator
*/
public ASEvaluation getEvaluator() {
return m_Evaluator;
}
/**
* Set the search method to test.
*
* @param value the search method to use.
*/
public void setSearch(ASSearch value) {
m_Search = value;
}
/**
* Get the current search method
*
* @return the current search method
*/
public ASSearch getSearch() {
return m_Search;
}
/**
* Sets whether the evaluator or the search method is being tested.
*
* @param value if true then the evaluator will be tested
*/
public void setTestEvaluator(boolean value) {
m_TestEvaluator = value;
}
/**
* Gets whether the evaluator is being tested or the search method.
*
* @return true if the evaluator is being tested
*/
public boolean getTestEvaluator() {
return m_TestEvaluator;
}
/**
* returns either the evaluator or the search method.
*
* @return the object to be tested
* @see #m_TestEvaluator
*/
protected Object getTestObject() {
if (getTestEvaluator())
return getEvaluator();
else
return getSearch();
}
/**
* returns deep copies of the given object
*
* @param obj the object to copy
* @param num the number of copies
* @return the deep copies
* @throws Exception if copying fails
*/
protected Object[] makeCopies(Object obj, int num) throws Exception {
if (obj == null)
throw new Exception("No object set");
Object[] objs = new Object[num];
SerializedObject so = new SerializedObject(obj);
for(int i = 0; i < objs.length; i++) {
objs[i] = so.getObject();
}
return objs;
}
/**
* Performs a attribute selection with the given search and evaluation scheme
* on the provided data. The generated AttributeSelection object is returned.
*
* @param search the search scheme to use
* @param eval the evaluator to use
* @param data the data to work on
* @return the used attribute selection object
* @throws Exception if the attribute selection fails
*/
protected AttributeSelection search(ASSearch search, ASEvaluation eval,
Instances data) throws Exception {
AttributeSelection result;
result = new AttributeSelection();
result.setSeed(42);
result.setSearch(search);
result.setEvaluator(eval);
result.SelectAttributes(data);
return result;
}
/**
* 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 scheme says it handles weights
* @param multiInstance true if the scheme handles multi-instance data
*/
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);
correctSearchInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance, classType);
datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, classType,
handleMissingPredictors, handleMissingClass);
}
}
/**
* Checks whether the scheme can take command line options.
*
* @return index 0 is true if the scheme can take options
*/
protected boolean[] canTakeOptions() {
boolean[] result = new boolean[2];
print("options...");
if (getTestObject() instanceof OptionHandler) {
println("yes");
if (m_Debug) {
println("\n=== Full report ===");
Enumeration enu = ((OptionHandler) getTestObject()).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 scheme handles instance weights
*/
protected boolean[] weightedInstancesHandler() {
boolean[] result = new boolean[2];
print("weighted instances scheme...");
if (getTestObject() 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 scheme handles multi-instance data
*/
protected boolean[] multiInstanceHandler() {
boolean[] result = new boolean[2];
print("multi-instance scheme...");
if (getTestObject() instanceof MultiInstanceCapabilitiesHandler) {
println("yes");
result[0] = true;
}
else {
println("no");
result[0] = false;
}
return result;
}
/**
* tests for a serialVersionUID. Fails in case the schemes don't declare
* a UID (both must!).
*
* @return index 0 is true if the scheme declares a UID
*/
protected boolean[] declaresSerialVersionUID() {
boolean[] result = new boolean[2];
boolean eval;
boolean search;
print("serialVersionUID...");
eval = !SerializationHelper.needsUID(m_Evaluator.getClass());
search = !SerializationHelper.needsUID(m_Search.getClass());
result[0] = eval && search;
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("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
* ASSearch.search is called. This test calls search with
* one training dataset. ASSearch is then called on a training set with
* different structure, and then again with the original training set.
* If the equals method of the ASEvaluation class returns false, this is
* noted as incorrect search 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 always false
*/
protected boolean[] correctSearchInitialisation(
boolean nominalPredictor,
boolean numericPredictor,
boolean stringPredictor,
boolean datePredictor,
boolean relationalPredictor,
boolean multiInstance,
int classType) {
boolean[] result = new boolean[2];
print("correct initialisation during search");
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;
ASSearch search = null;
ASEvaluation evaluation1A = null;
ASEvaluation evaluation1B = null;
ASEvaluation evaluation2 = null;
AttributeSelection attsel1A = null;
AttributeSelection attsel1B = 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() : 0,
datePredictor ? getNumDate() : 0,
relationalPredictor ? getNumRelational() : 0,
numClasses,
classType,
multiInstance);
if (missingLevel > 0) {
addMissing(train1, missingLevel, predictorMissing, classMissing);
addMissing(train2, missingLevel, predictorMissing, classMissing);
}
search = ASSearch.makeCopies(getSearch(), 1)[0];
evaluation1A = ASEvaluation.makeCopies(getEvaluator(), 1)[0];
evaluation1B = ASEvaluation.makeCopies(getEvaluator(), 1)[0];
evaluation2 = ASEvaluation.makeCopies(getEvaluator(), 1)[0];
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
stage = 0;
attsel1A = search(search, evaluation1A, train1);
stage = 1;
search(search, evaluation2, train2);
stage = 2;
attsel1B = search(search, evaluation1B, train1);
stage = 3;
if (!attsel1A.toResultsString().equals(attsel1B.toResultsString())) {
if (m_Debug) {
println(
"\n=== Full report ===\n"
+ "\nFirst search\n"
+ evaluation1A.toString()
+ "\n\n");
println(
"\nSecond search\n"
+ evaluation1B.toString()
+ "\n\n");
}
throw new Exception("Results differ between search calls");
}
println("yes");
result[0] = true;
if (false && m_Debug) {
println(
"\n=== Full report ===\n"
+ "\nFirst search\n"
+ evaluation1A.toString()
+ "\n\n");
println(
"\nSecond search\n"
+ evaluation1B.toString()
+ "\n\n");
}
}
catch (Exception ex) {
println("no");
result[0] = false;
if (m_Debug) {
println("\n=== Full Report ===");
print("Problem during 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("=== 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");
accepts.addElement("no attributes");
int numTrain = getNumInstances(), numClasses = 2;
return runBasicTest(nominalPredictor, numericPredictor, stringPredictor,
datePredictor, relationalPredictor,
multiInstance,
classType,
missingLevel, predictorMissing, classMissing,
numTrain, numClasses,
accepts);
}
/**
* Checks whether the scheme can handle instance weights.
* This test compares the scheme performance on two datasets
* that are identical except for the training weights. If the
* results change, then the scheme 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 scheme 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("scheme 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;
ASSearch[] search = null;
ASEvaluation evaluationB = null;
ASEvaluation evaluationI = null;
AttributeSelection attselB = null;
AttributeSelection attselI = 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);
search = ASSearch.makeCopies(getSearch(), 2);
evaluationB = ASEvaluation.makeCopies(getEvaluator(), 1)[0];
evaluationI = ASEvaluation.makeCopies(getEvaluator(), 1)[0];
attselB = search(search[0], evaluationB, 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);
}
attselI = search(search[1], evaluationI, train);
if (attselB.toResultsString().equals(attselI.toResultsString())) {
// 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("\nboth methods\n");
println(evaluationB.toString());
} else {
print("Problem during training");
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
* 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 scheme can handle
* (at least) moderate missing predictor values
* @param classMissing true if we know the scheme 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("scheme 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;
Instances trainCopy = null;
ASSearch search = null;
ASEvaluation evaluation = 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);
search = ASSearch.makeCopies(getSearch(), 1)[0];
evaluation = ASEvaluation.makeCopies(getEvaluator(), 1)[0];
trainCopy = new Instances(train);
} catch (Exception ex) {
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
search(search, evaluation, 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 training");
println(": " + ex.getMessage() + "\n");
println("Here are the datasets:\n");
println("=== Train Dataset (original) ===\n"
+ trainCopy.toString() + "\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;
ASSearch search = null;
ASEvaluation evaluation = 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);
search = ASSearch.makeCopies(getSearch(), 1)[0];
evaluation = ASEvaluation.makeCopies(getEvaluator(), 1)[0];
} catch (Exception ex) {
ex.printStackTrace();
throw new Error("Error setting up for tests: " + ex.getMessage());
}
try {
search(search, evaluation, 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 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 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);
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: 1.7 $");
}
/**
* Test method for this class
*
* @param args the commandline parameters
*/
public static void main(String [] args) {
runCheck(new CheckAttributeSelection(), args);
}
}
The table below shows all metrics for CheckAttributeSelection.java.
| Metric | Value | Description | |
|---|---|---|---|
| BLOCKS | 90.00 | Number of blocks | |
| BLOCK_COMMENT | 28.00 | Number of block comment lines | |
| COMMENTS | 589.00 | Comment lines | |
| COMMENT_DENSITY | 0.71 | Comment density | |
| COMPARISONS | 95.00 | Number of comparison operators | |
| CYCLOMATIC | 147.00 | Cyclomatic complexity | |
| DECL_COMMENTS | 40.00 | Comments in declarations | |
| DOC_COMMENT | 557.00 | Number of javadoc comment lines | |
| ELOC | 830.00 | Effective lines of code | |
| EXEC_COMMENTS | 4.00 | Comments in executable code | |
| EXITS | 113.00 | Procedure exits | |
| FUNCTIONS | 33.00 | Number of function declarations | |
| HALSTEAD_DIFFICULTY | 98.54 | Halstead difficulty | |
| HALSTEAD_EFFORT | 0.00 | Halstead effort | |
| INTERFACE_COMPLEXITY | 180.00 | Interface complexity | |
| JAVA0001 | 1.00 | JAVA0001 Package name does not contain only lower case letters | |
| JAVA0002 | 1.00 | JAVA0002 Package name does not begin with a top level domain name or country code | |
| JAVA0003 | 0.00 | JAVA0003 Minimize use of on-demand (.*) imports | |
| JAVA0004 | 0.00 | JAVA0004 Unnecessary import from java.lang | |
| JAVA0005 | 1.00 | JAVA0005 Imports not in specified order | |
| JAVA0006 | 0.00 | JAVA0006 Empty finally block | |
| JAVA0007 | 0.00 | JAVA0007 Should not declare public field | |
| JAVA0008 | 0.00 | JAVA0008 Empty catch block | |
| JAVA0009 | 0.00 | JAVA0009 Protected member in final class | |
| JAVA0010 | 0.00 | JAVA0010 Non-instantiable class does not contain a non-private static member | |
| JAVA0011 | 0.00 | JAVA0011 Abstract class does not contain an abstract method | |
| JAVA0012 | 0.00 | JAVA0012 Non-constructor method with same name as declaring class | |
| JAVA0013 | 0.00 | JAVA0013 Non-blank final field is not static | |
| JAVA0014 | 0.00 | JAVA0014 Class with only static members has non-private constructor | |
| JAVA0015 | 0.00 | JAVA0015 Package class contains public nested type | |
| JAVA0016 | 0.00 | JAVA0016 Abstract class contains public constructor | |
| JAVA0017 | 0.00 | JAVA0017 Class name does not have required form | |
| JAVA0018 | 0.00 | JAVA0018 Method name does not have required form | |
| JAVA0019 | 0.00 | JAVA0019 Interface name does not have required form | |
| JAVA0020 | 0.00 | JAVA0020 Field name does not have required form | |
| JAVA0021 | 0.00 | JAVA0021 Interface method name does not have required form | |
| JAVA0022 | 0.00 | JAVA0022 Static final field name does not have required form | |
| JAVA0023 | 0.00 | JAVA0023 Empty finalize method | |
| JAVA0024 | 0.00 | JAVA0024 Empty class | |
| JAVA0025 | 0.00 | JAVA0025 Method override is empty | |
| JAVA0026 | 0.00 | JAVA0026 Finalize method with parameters | |
| JAVA0029 | 0.00 | JAVA0029 Private method not used | |
| JAVA0030 | 0.00 | JAVA0030 Private field not used | |
| JAVA0031 | 0.00 | JAVA0031 Case statement not properly closed | |
| JAVA0032 | 2.00 | JAVA0032 Switch statement missing default | |
| JAVA0033 | 0.00 | JAVA0033 default: not last case in switch statement | |
| JAVA0034 | 33.00 | JAVA0034 Missing braces in if statement | |
| JAVA0035 | 1.00 | JAVA0035 Missing braces in for statement | |
| JAVA0036 | 3.00 | JAVA0036 Missing braces in while statement | |
| JAVA0038 | 0.00 | JAVA0038 Non-case label in switch statement | |
| JAVA0039 | 0.00 | JAVA0039 Break statement with label | |
| JAVA0040 | 0.00 | JAVA0040 Switch statement contains N cases (maximum: M) | |
| JAVA0041 | 0.00 | JAVA0041 Nested synchronized block | |
| JAVA0042 | 0.00 | JAVA0042 Empty synchronized statement | |
| JAVA0043 | 0.00 | JAVA0043 Inner class does not use outer class | |
| JAVA0044 | 0.00 | JAVA0044 Serializable class with no instance variables | |
| JAVA0045 | 0.00 | JAVA0045 Serializable class with only transient fields | |
| JAVA0046 | 0.00 | JAVA0046 Name of class not derived from Exception ends with 'Exception' | |
| JAVA0047 | 0.00 | JAVA0047 Serializable class derives from invalid base class | |
| JAVA0048 | 0.00 | JAVA0048 Name of class derived from Exception does not end with 'Exception' | |
| JAVA0049 | 1.00 | JAVA0049 Nested block at depth N (maximum: M) | |
| JAVA0050 | 0.00 | JAVA0050 Class derives from java.lang.Error | |
| JAVA0051 | 0.00 | JAVA0051 Class derives from java.lang.RuntimeException | |
| JAVA0052 | 0.00 | JAVA0052 Class derives from java.lang.Throwable | |
| JAVA0053 | 0.00 | JAVA0053 Unused label | |
| JAVA0054 | 0.00 | JAVA0054 Inheritance depth N exceeds maximum M | |
| JAVA0055 | 0.00 | JAVA0055 Class should be interface | |
| JAVA0056 | 0.00 | JAVA0056 Unnecessary abstract modifier for interface or annotation | |
| JAVA0057 | 0.00 | JAVA0057 Unnecessary default constructor | |
| JAVA0058 | 0.00 | JAVA0058 Constructor calls super() | |
| JAVA0059 | 0.00 | JAVA0059 Method override only calls super() | |
| JAVA0061 | 0.00 | JAVA0061 Inaccessible member in anonymous class | |
| JAVA0062 | 0.00 | JAVA0062 Public class missing public member or protected constructor | |
| JAVA0063 | 0.00 | JAVA0063 Identifier name should not contain '$' | |
| JAVA0064 | 0.00 | JAVA0064 N variations of identifier name (maximum: M) | |
| JAVA0065 | 0.00 | JAVA0065 Unnecessary final modifier for method in final class | |
| JAVA0066 | 0.00 | JAVA0066 Unnecessary modifier for interface nested type | |
| JAVA0067 | 0.00 | JAVA0067 Array descriptor on identifier name | |
| JAVA0068 | 0.00 | JAVA0068 Modifiers not declared in recommended order | |
| JAVA0071 | 0.00 | JAVA0071 Strings compared with == | |
| JAVA0073 | 0.00 | JAVA0073 Integer division in floating-point context | |
| JAVA0074 | 0.00 | JAVA0074 Use of Object.notify() | |
| JAVA0075 | 0.00 | JAVA0075 Method parameter hides field | |
| JAVA0076 | 11.00 | JAVA0076 Use of magic number | |
| JAVA0077 | 0.00 | JAVA0077 Private field not used in declaring class | |
| JAVA0078 | 0.00 | JAVA0078 Floating point values compared with == | |
| JAVA0079 | 0.00 | JAVA0079 Use of instance to reference static member | |
| JAVA0080 | 0.00 | JAVA0080 Import declaration not used | |
| JAVA0081 | 0.00 | JAVA0081 Boolean literal in comparison | |
| JAVA0082 | 0.00 | JAVA0082 Unnecessary widening cast | |
| JAVA0083 | 0.00 | JAVA0083 Unnecessary instanceof test | |
| JAVA0084 | 0.00 | JAVA0084 Should use compound assignment operator | |
| JAVA0085 | 0.00 | JAVA0085 Use of sun.* class | |
| JAVA0087 | 0.00 | JAVA0087 Use of Thread.sleep() | |
| JAVA0089 | 0.00 | JAVA0089 Use of restricted package | |
| JAVA0092 | 0.00 | JAVA0092 Use of restricted type | |
| JAVA0093 | 0.00 | JAVA0093 Redundant assignment | |
| JAVA0094 | 0.00 | JAVA0094 Field hides a superclass field | |
| JAVA0095 | 0.00 | JAVA0095 Uninitialized private field | |
| JAVA0096 | 0.00 | JAVA0096 Field in nested class hides outer field | |
| JAVA0098 | 0.00 | JAVA0098 Minimize use of implicit field initializers | |
| JAVA0100 | 0.00 | JAVA0100 Class contains N non-final fields (maximum: M) | |
| JAVA0101 | 0.00 | JAVA0101 Unnecessary modifier for field in interface | |
| JAVA0102 | 0.00 | JAVA0102 Last statement in finalize() not super.finalize() | |
| JAVA0103 | 0.00 | JAVA0103 Explicit call to finalize() | |
| JAVA0104 | 0.00 | JAVA0104 finalize() only calls super.finalize() | |
| JAVA0105 | 0.00 | JAVA0105 Duplicate import declaration | |
| JAVA0106 | 0.00 | JAVA0106 Unnecessary import from current package | |
| JAVA0108 | 0.00 | JAVA0108 Incorrect javadoc: no @param tag for 'parameter' | |
| JAVA0109 | 0.00 | JAVA0109 Incorrect javadoc: no parameter 'parameter' | |
| JAVA0110 | 0.00 | JAVA0110 Incorrect javadoc: no @return tag | |
| JAVA0111 | 0.00 | JAVA0111 Incorrect javadoc: @return tag for void method | |
| JAVA0112 | 0.00 | JAVA0112 Incorrect javadoc: no exception 'exception' in throws | |
| JAVA0113 | 0.00 | JAVA0113 Incorrect javadoc: no @author tag | |
| JAVA0114 | 0.00 | JAVA0114 Incorrect javadoc: no @version tag | |
| JAVA0115 | 0.00 | JAVA0115 Incorrect javadoc: no @throws or @exception tag for 'exception' | |
| JAVA0116 | 0.00 | JAVA0116 Missing javadoc: field 'field' | |
| JAVA0117 | 0.00 | JAVA0117 Missing javadoc: method 'method' | |
| JAVA0118 | 0.00 | JAVA0118 Missing javadoc: type 'type' | |
| JAVA0119 | 0.00 | JAVA0119 Control variable changed within body of for loop | |
| JAVA0123 | 0.00 | JAVA0123 Use all three components of for loop | |
| JAVA0125 | 0.00 | JAVA0125 Continue statement with label | |
| JAVA0126 | 0.00 | JAVA0126 Method declares unchecked exception in throws | |
| JAVA0128 | 0.00 | JAVA0128 Public constructor in non-public class | |
| JAVA0130 | 0.00 | JAVA0130 Non-static method does not use instance fields | |
| JAVA0131 | 0.00 | JAVA0131 Compatible method does not override base | |
| JAVA0132 | 0.00 | JAVA0132 Method overload with compatible signature | |
| JAVA0133 | 0.00 | JAVA0133 Non-synchronized method overrides synchronized method | |
| JAVA0135 | 0.00 | JAVA0135 Only one of Object.equals and Object.hashCode defined: missing 'method' | |
| JAVA0136 | 1.00 | JAVA0136 N methods defined in class (maximum: M) | |
| JAVA0137 | 0.00 | JAVA0137 Non-abstract class missing constructor | |
| JAVA0138 | 13.00 | JAVA0138 N parameters defined for method (maximum: M) | |
| JAVA0139 | 0.00 | JAVA0139 Definition of main other than public static void main(java.lang.String[]) | |
| JAVA0141 | 0.00 | JAVA0141 Unnecessary modifier for method in interface | |
| JAVA0143 | 0.00 | JAVA0143 Synchronized method | |
| JAVA0144 | 0.00 | JAVA0144 Line exceeds maximum M characters | |
| JAVA0145 | 111.00 | JAVA0145 Tab character used in source file | |
| JAVA0150 | 4.00 | JAVA0150 java.lang.Error (or subclass) thrown | |
| JAVA0153 | 0.00 | JAVA0153 Inefficient conversion of integer to string | |
| JAVA0159 | 0.00 | JAVA0159 Inefficient conversion of string to integer | |
| JAVA0160 | 0.00 | JAVA0160 Method does not throw specified exception | |
| JAVA0161 | 0.00 | JAVA0161 Conditional wait() not in loop | |
| JAVA0163 | 0.00 | JAVA0163 Empty statement | |
| JAVA0165 | 0.00 | JAVA0165 Conflicting return statement in finally block | |
| JAVA0166 | 8.00 | JAVA0166 Generic exception caught | |
| JAVA0167 | 0.00 | JAVA0167 ThreadDeath not rethrown | |
| JAVA0169 | 0.00 | JAVA0169 Unnecessary catch block: exception 'exception' | |
| JAVA0170 | 0.00 | JAVA0170 Caught exception not derived from java.lang.Exception | |
| JAVA0171 | 0.00 | JAVA0171 Unused local variable | |
| JAVA0173 | 1.00 | JAVA0173 Unused method parameter | |
| JAVA0174 | 0.00 | JAVA0174 Assigned local variable never used | |
| JAVA0175 | 0.00 | JAVA0175 Successive assignment to variable | |
| JAVA0176 | 0.00 | JAVA0176 Local variable name does not have required form | |
| JAVA0177 | 10.00 | JAVA0177 Variable declaration missing initializer | |
| JAVA0179 | 0.00 | JAVA0179 Local variable hides visible field | |
| JAVA0233 | 0.00 | JAVA0233 Definition of serialVersionUID other than 'private static final long serialVersionUID' | |
| JAVA0234 | 0.00 | JAVA0234 Class is Serializable but does not define serialVersionUID | |
| JAVA0235 | 0.00 | JAVA0235 Class defines serialVersionUID but does not implement Serializable | |
| JAVA0236 | 0.00 | JAVA0236 Attempt to clone an object which does not implement Cloneable | |
| JAVA0237 | 0.00 | JAVA0237 Class implements Cloneable but does not have public clone method | |
| JAVA0238 | 0.00 | JAVA0238 Clone method does not call super.clone() | |
| JAVA0239 | 0.00 | JAVA0239 Class declares 'readObject' or 'writeObject' but does not implement Serializable | |
| JAVA0240 | 0.00 | JAVA0240 Serializable class which declares readObject or writeObject but not both | |
| JAVA0241 | 0.00 | JAVA0241 'readObject' or 'writeObject' should be declared private in Serializable class | |
| JAVA0242 | 0.00 | JAVA0242 Transient field in non-Serializable class | |
| JAVA0243 | 0.00 | JAVA0243 'readResolve' or 'writeReplace' should be declared private or protected | |
| JAVA0244 | 0.00 | JAVA0244 Field or method name in subclass differs only by case from inherited field or method | |
| JAVA0245 | 0.00 | JAVA0245 JUnit TestCase with non-trivial constructor | |
| JAVA0246 | 0.00 | JAVA0246 JUnit assertXXX statement missing message parameter | |
| JAVA0247 | 0.00 | JAVA0247 JUnit 'setUp()' and 'tearDown()' should call super method | |
| JAVA0248 | 0.00 | JAVA0248 JUnit method 'setUp' or 'tearDown' with incorrect signature | |
| JAVA0249 | 0.00 | JAVA0249 JUnit TestCase 'suite()' should be declared static | |
| JAVA0250 | 0.00 | JAVA0250 JUnit TestCase declares testXXX method with incorrect signature | |
| JAVA0251 | 0.00 | JAVA0251 Use '%n' for line breaks in printf/format for platform independence | |
| JAVA0252 | 0.00 | JAVA0252 'enum' is a Java 1.5 reserved word | |
| JAVA0253 | 0.00 | JAVA0253 Not all enum constants consumed in switch statement | |
| JAVA0254 | 0.00 | JAVA0254 Use enhanced for loop construct instead of Iterator | |
| JAVA0255 | 0.00 | JAVA0255 Result of method invocation not used | |
| JAVA0256 | 0.00 | JAVA0256 Assignment of external collection/array to field | |
| JAVA0257 | 0.00 | JAVA0257 Use of 'Constant Interface' anti-pattern | |
| JAVA0258 | 0.00 | JAVA0258 Implement Iterable for foreach compatibility | |
| JAVA0259 | 0.00 | JAVA0259 Return of collection/array field | |
| JAVA0260 | 0.00 | JAVA0260 Use 'enum' instead of Enumerated Type pattern | |
| JAVA0261 | 0.00 | JAVA0261 Use specialized Enum collection types | |
| JAVA0262 | 0.00 | JAVA0262 Use of char in integer context | |
| JAVA0263 | 0.00 | JAVA0263 Long literal ends with 'l' instead of 'L' | |
| JAVA0264 | 0.00 | JAVA0264 Integer math in long context - check for overflow | |
| JAVA0265 | 1.00 | JAVA0265 Use of Throwable.printStackTrace() | |
| JAVA0266 | 0.00 | JAVA0266 Use of System.out | |
| JAVA0267 | 0.00 | JAVA0267 Use of System.err | |
| JAVA0269 | 0.00 | JAVA0269 Contents of StringBuffer never used | |
| JAVA0270 | 0.00 | JAVA0270 Use Java 5.0 enhanced for loop construct to iterate over all elements in an array | |
| JAVA0271 | 0.00 | JAVA0271 Minimize use of on-demand (.*) static imports | |
| JAVA0272 | 0.00 | JAVA0272 Thread.run() called | |
| JAVA0273 | 0.00 | JAVA0273 Non-final derivative of Thread calls start() in constructor | |
| JAVA0274 | 0.00 | JAVA0274 Serializable class has a synchronized readObject() | |
| JAVA0275 | 0.00 | JAVA0275 Serializable class has a synchronized writeObject() and no other synchronized methods | |
| JAVA0276 | 0.00 | JAVA0276 Unnecessary use of String constructor | |
| JAVA0277 | 0.00 | JAVA0277 Iterator.next() implementation does not throw NoSuchElementException | |
| JAVA0278 | 0.00 | JAVA0278 Unnecessary use of Boolean constructor | |
| JAVA0279 | 0.00 | JAVA0279 Serialization method readObject or readObjectNoData calls an overridable method | |
| JAVA0280 | 0.00 | JAVA0280 IllegalMonitorStateException caught | |
| JAVA0281 | 0.00 | JAVA0281 Iterator.next() not called in loop | |
| JAVA0282 | 0.00 | JAVA0282 Call to Iterator.next() in loop which does not test Iterator.hasNext() | |
| JAVA0283 | 0.00 | JAVA0283 Control variable not updated in loop body | |
| JAVA0284 | 0.00 | JAVA0284 Explicit garbage collection | |
| JAVA0285 | 0.00 | JAVA0285 Dereference of potentially null variable | |
| JAVA0286 | 0.00 | JAVA0286 Dereference of null variable | |
| JAVA0287 | 0.00 | JAVA0287 Unnecessary null check | |
| JAVA0288 | 0.00 | JAVA0288 Inconsistent null check | |
| LINES | 1642.00 | Number of lines in the source file | |
| LINE_COMMENT | 4.00 | Number of line comments | |
| LOC | 916.00 | Lines of code | |
| LOGICAL_LINES | 423.00 | Number of statements | |
| LOOPS | 11.00 | Number of loops | |
| NEST_DEPTH | 7.00 | Maximum nesting depth | |
| OPERANDS | 1917.00 | Number of operands | |
| OPERATORS | 3503.00 | Number of operators | |
| PARAMS | 130.00 | Number of formal parameter declarations | |
| PROGRAM_LENGTH | 5420.00 | Halstead program length | |
| PROGRAM_VOCAB | 590.00 | Halstead program vocabulary | |
| PROGRAM_VOLUME | 0.00 | Halstead program volume | |
| RETURNS | 50.00 | Number of return points from functions | |
| SIZE | 57211.00 | Size of the file in bytes | |
| UNIQUE_OPERANDS | 535.00 | Number of unique operands | |
| UNIQUE_OPERATORS | 55.00 | Number of unique operators | |
| WHITESPACE | 137.00 | Number of whitespace lines | |