source: branches/MetisMQI/src/main/java/weka/associations/CheckAssociator.java

/*
 *    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. <p/>
 * 
 * Typical usage: <p/>
 * <code>java weka.associations.CheckAssociator -W associator_name 
 * -- associator_options </code><p/>
 * 
 * CheckAssociator reports on the following:
 * <ul>
 *    <li> Associator abilities 
 *      <ul>
 *         <li> Possible command line options to the associators </li>
 *         <li> Whether the associators can predict nominal, numeric, string, 
 *              date or relational class attributes. </li>
 *         <li> Whether the associators can handle numeric predictor attributes </li>
 *         <li> Whether the associators can handle nominal predictor attributes </li>
 *         <li> Whether the associators can handle string predictor attributes </li>
 *         <li> Whether the associators can handle date predictor attributes </li>
 *         <li> Whether the associators can handle relational predictor attributes </li>
 *         <li> Whether the associators can handle multi-instance data </li>
 *         <li> Whether the associators can handle missing predictor values </li>
 *         <li> Whether the associators can handle missing class values </li>
 *         <li> Whether a nominal associators only handles 2 class problems </li>
 *         <li> Whether the associators can handle instance weights </li>
 *      </ul>
 *    </li>
 *    <li> Correct functioning 
 *      <ul>
 *         <li> Correct initialisation during buildAssociations (i.e. no result
 *              changes when buildAssociations called repeatedly) </li>
 *         <li> Whether the associators alters the data pased to it 
 *              (number of instances, instance order, instance weights, etc) </li>
 *      </ul>
 *    </li>
 *    <li> Degenerate cases 
 *      <ul>
 *         <li> building associators with zero training 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 CheckAssociator with the debug option set will output the 
 * training dataset for any failed tests.<p/>
 *
 * The <code>weka.associations.AbstractAssociatorTest</code> uses this
 * class to test all the associators. 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 &lt;num&gt;
 *  The number of instances in the datasets (default 20).</pre>
 * 
 * <pre> -nominal &lt;num&gt;
 *  The number of nominal attributes (default 2).</pre>
 * 
 * <pre> -nominal-values &lt;num&gt;
 *  The number of values for nominal attributes (default 1).</pre>
 * 
 * <pre> -numeric &lt;num&gt;
 *  The number of numeric attributes (default 1).</pre>
 * 
 * <pre> -string &lt;num&gt;
 *  The number of string attributes (default 1).</pre>
 * 
 * <pre> -date &lt;num&gt;
 *  The number of date attributes (default 1).</pre>
 * 
 * <pre> -relational &lt;num&gt;
 *  The number of relational attributes (default 1).</pre>
 * 
 * <pre> -num-instances-relational &lt;num&gt;
 *  The number of instances in relational/bag attributes (default 10).</pre>
 * 
 * <pre> -words &lt;comma-separated-list&gt;
 *  The words to use in string attributes.</pre>
 * 
 * <pre> -word-separators &lt;chars&gt;
 *  The word separators to use in string attributes.</pre>
 * 
 * <pre> -W
 *  Full name of the associator analysed.
 *  eg: weka.associations.Apriori
 *  (default weka.associations.Apriori)</pre>
 * 
 * <pre> 
 * Options specific to associator weka.associations.Apriori:
 * </pre>
 * 
 * <pre> -N &lt;required number of rules output&gt;
 *  The required number of rules. (default = 10)</pre>
 * 
 * <pre> -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
 *  The metric type by which to rank rules. (default = confidence)</pre>
 * 
 * <pre> -C &lt;minimum metric score of a rule&gt;
 *  The minimum confidence of a rule. (default = 0.9)</pre>
 * 
 * <pre> -D &lt;delta for minimum support&gt;
 *  The delta by which the minimum support is decreased in
 *  each iteration. (default = 0.05)</pre>
 * 
 * <pre> -U &lt;upper bound for minimum support&gt;
 *  Upper bound for minimum support. (default = 1.0)</pre>
 * 
 * <pre> -M &lt;lower bound for minimum support&gt;
 *  The lower bound for the minimum support. (default = 0.1)</pre>
 * 
 * <pre> -S &lt;significance level&gt;
 *  If used, rules are tested for significance at
 *  the given level. Slower. (default = no significance testing)</pre>
 * 
 * <pre> -I
 *  If set the itemsets found are also output. (default = no)</pre>
 * 
 * <pre> -R
 *  Remove columns that contain all missing values (default = no)</pre>
 * 
 * <pre> -V
 *  Report progress iteratively. (default = no)</pre>
 * 
 * <pre> -A
 *  If set class association rules are mined. (default = no)</pre>
 * 
 * <pre> -c &lt;the class index&gt;
 *  The class index. (default = last)</pre>
 * 
 <!-- options-end -->
 *
 * Options after -- are passed to the designated associator.<p/>
 *
 * @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 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. 
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -D
   *  Turn on debugging output.</pre>
   * 
   * <pre> -S
   *  Silent mode - prints nothing to stdout.</pre>
   * 
   * <pre> -N &lt;num&gt;
   *  The number of instances in the datasets (default 20).</pre>
   * 
   * <pre> -nominal &lt;num&gt;
   *  The number of nominal attributes (default 2).</pre>
   * 
   * <pre> -nominal-values &lt;num&gt;
   *  The number of values for nominal attributes (default 1).</pre>
   * 
   * <pre> -numeric &lt;num&gt;
   *  The number of numeric attributes (default 1).</pre>
   * 
   * <pre> -string &lt;num&gt;
   *  The number of string attributes (default 1).</pre>
   * 
   * <pre> -date &lt;num&gt;
   *  The number of date attributes (default 1).</pre>
   * 
   * <pre> -relational &lt;num&gt;
   *  The number of relational attributes (default 1).</pre>
   * 
   * <pre> -num-instances-relational &lt;num&gt;
   *  The number of instances in relational/bag attributes (default 10).</pre>
   * 
   * <pre> -words &lt;comma-separated-list&gt;
   *  The words to use in string attributes.</pre>
   * 
   * <pre> -word-separators &lt;chars&gt;
   *  The word separators to use in string attributes.</pre>
   * 
   * <pre> -W
   *  Full name of the associator analysed.
   *  eg: weka.associations.Apriori
   *  (default weka.associations.Apriori)</pre>
   * 
   * <pre> 
   * Options specific to associator weka.associations.Apriori:
   * </pre>
   * 
   * <pre> -N &lt;required number of rules output&gt;
   *  The required number of rules. (default = 10)</pre>
   * 
   * <pre> -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
   *  The metric type by which to rank rules. (default = confidence)</pre>
   * 
   * <pre> -C &lt;minimum metric score of a rule&gt;
   *  The minimum confidence of a rule. (default = 0.9)</pre>
   * 
   * <pre> -D &lt;delta for minimum support&gt;
   *  The delta by which the minimum support is decreased in
   *  each iteration. (default = 0.05)</pre>
   * 
   * <pre> -U &lt;upper bound for minimum support&gt;
   *  Upper bound for minimum support. (default = 1.0)</pre>
   * 
   * <pre> -M &lt;lower bound for minimum support&gt;
   *  The lower bound for the minimum support. (default = 0.1)</pre>
   * 
   * <pre> -S &lt;significance level&gt;
   *  If used, rules are tested for significance at
   *  the given level. Slower. (default = no significance testing)</pre>
   * 
   * <pre> -I
   *  If set the itemsets found are also output. (default = no)</pre>
   * 
   * <pre> -R
   *  Remove columns that contain all missing values (default = no)</pre>
   * 
   * <pre> -V
   *  Report progress iteratively. (default = no)</pre>
   * 
   * <pre> -A
   *  If set class association rules are mined. (default = no)</pre>
   * 
   * <pre> -c &lt;the class index&gt;
   *  The class index. (default = last)</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;
    
    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);
  }
}