content: /*
   *    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 &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> -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 &lt;eval|search&gt;
   *  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 &lt;start set&gt;
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.
   *  Any starting attributes specified are
   *  ignored during the ranking.</pre>
   * 
   * <pre> -T &lt;threshold&gt;
   *  Specify a theshold by which attributes
   *  may be discarded from the ranking.</pre>
   * 
   * <pre> -N &lt;num to select&gt;
   *  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: 4783 $
   * @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 &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> -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 &lt;eval|search&gt;
     *  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 &lt;start set&gt;
     *  Specify a starting set of attributes.
     *  Eg. 1,3,5-7.
     *  Any starting attributes specified are
     *  ignored during the ranking.</pre>
     * 
     * <pre> -T &lt;threshold&gt;
     *  Specify a theshold by which attributes
     *  may be discarded from the ranking.</pre>
     * 
     * <pre> -N &lt;num to select&gt;
     *  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"
                + attsel1A.toResultsString()
                + "\n\n");
            println(
                "\nSecond search\n"
                + attsel1B.toResultsString()
                + "\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: 4783 $");
    }
    
    /**
     * Test method for this class
     * 
     * @param args the commandline parameters
     */
    public static void main(String [] args) {
      runCheck(new CheckAttributeSelection(), args);
    }
  }
  
  