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.
   */
  
  /*
   * CheckClusterer.java
   * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.clusterers;
  
  import weka.core.CheckScheme;
  import weka.core.FastVector;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.MultiInstanceCapabilitiesHandler;
  import weka.core.Option;
  import weka.core.OptionHandler;
  import weka.core.RevisionUtils;
  import weka.core.SerializationHelper;
  import weka.core.TestInstances;
  import weka.core.Utils;
  import weka.core.WeightedInstancesHandler;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   * Class for examining the capabilities and finding problems with 
   * clusterers. If you implement a clusterer 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 clusterer don't exist, but this will help find some
   * common ones. <p/>
   * 
   * Typical usage: <p/>
   * <code>java weka.clusterers.CheckClusterer -W clusterer_name 
   * -- clusterer_options </code><p/>
   * 
   * CheckClusterer reports on the following:
   * <ul>
   *    <li> Clusterer abilities 
   *      <ul>
   *         <li> Possible command line options to the clusterer </li>
   *         <li> Whether the clusterer can predict nominal, numeric, string, 
   *              date or relational class attributes.</li>
   *         <li> Whether the clusterer can handle numeric predictor attributes </li>
   *         <li> Whether the clusterer can handle nominal predictor attributes </li>
   *         <li> Whether the clusterer can handle string predictor attributes </li>
   *         <li> Whether the clusterer can handle date predictor attributes </li>
   *         <li> Whether the clusterer can handle relational predictor attributes </li>
   *         <li> Whether the clusterer can handle multi-instance data </li>
   *         <li> Whether the clusterer can handle missing predictor values </li>
   *         <li> Whether the clusterer can handle instance weights </li>
   *      </ul>
   *    </li>
   *    <li> Correct functioning 
   *      <ul>
   *         <li> Correct initialisation during buildClusterer (i.e. no result
   *              changes when buildClusterer called repeatedly) </li>
   *         <li> Whether the clusterer alters the data pased to it 
   *              (number of instances, instance order, instance weights, etc) </li>
   *      </ul>
   *    </li>
   *    <li> Degenerate cases 
   *      <ul>
   *         <li> building clusterer 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 CheckClusterer with the debug option set will output the 
   * training dataset for any failed tests.<p/>
   *
   * The <code>weka.clusterers.AbstractClustererTest</code> uses this
   * class to test all the clusterers. 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 clusterer analyzed.
   *  eg: weka.clusterers.SimpleKMeans
   *  (default weka.clusterers.SimpleKMeans)</pre>
   * 
   * <pre> 
   * Options specific to clusterer weka.clusterers.SimpleKMeans:
   * </pre>
   * 
   * <pre> -N &lt;num&gt;
   *  number of clusters.
   *  (default 2).</pre>
   * 
   * <pre> -V
   *  Display std. deviations for centroids.
   * </pre>
   * 
   * <pre> -M
   *  Replace missing values with mean/mode.
   * </pre>
   * 
   * <pre> -S &lt;num&gt;
   *  Random number seed.
   *  (default 10)</pre>
   * 
   <!-- options-end -->
   *
   * Options after -- are passed to the designated clusterer.<p/>
   *
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @author FracPete (fracpete at waikato dot ac dot nz)
   * @version $Revision: 1.11 $
   * @see TestInstances
   */
  public class CheckClusterer 
    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 clusterer to be examined */
    protected Clusterer m_Clusterer = new SimpleKMeans();
    
    /**
     * default constructor
     */
    public CheckClusterer() {
      super();
      
      setNumInstances(40);
    }
    
    /**
     * 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 clusterer analyzed.\n"
          +"\teg: weka.clusterers.SimpleKMeans\n"
          + "\t(default weka.clusterers.SimpleKMeans)",
          "W", 1, "-W"));
      
      if ((m_Clusterer != null) 
          && (m_Clusterer instanceof OptionHandler)) {
        result.addElement(new Option("", "", 0, 
            "\nOptions specific to clusterer "
            + m_Clusterer.getClass().getName()
            + ":"));
        Enumeration enu = ((OptionHandler)m_Clusterer).listOptions();
        while (enu.hasMoreElements())
          result.addElement(enu.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> -W
     *  Full name of the clusterer analyzed.
     *  eg: weka.clusterers.SimpleKMeans
     *  (default weka.clusterers.SimpleKMeans)</pre>
     * 
     * <pre> 
     * Options specific to clusterer weka.clusterers.SimpleKMeans:
     * </pre>
     * 
     * <pre> -N &lt;num&gt;
     *  number of clusters.
     *  (default 2).</pre>
     * 
     * <pre> -V
     *  Display std. deviations for centroids.
     * </pre>
     * 
     * <pre> -M
     *  Replace missing values with mean/mode.
     * </pre>
     * 
     * <pre> -S &lt;num&gt;
     *  Random number seed.
     *  (default 10)</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;
      
      tmpStr = Utils.getOption('N', options);
      
      super.setOptions(options);
      
      if (tmpStr.length() != 0)
        setNumInstances(Integer.parseInt(tmpStr));
      else
        setNumInstances(40);
  
      tmpStr = Utils.getOption('W', options);
      if (tmpStr.length() == 0)
        tmpStr = weka.clusterers.SimpleKMeans.class.getName();
      setClusterer(
  	(Clusterer) forName(
  	    "weka.clusterers", 
  	    Clusterer.class, 
  	    tmpStr, 
  	    Utils.partitionOptions(options)));
    }
    
    /**
     * Gets the current settings of the CheckClusterer.
     *
     * @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 (getClusterer() != null) {
        result.add("-W");
        result.add(getClusterer().getClass().getName());
      }
      
      if ((m_Clusterer != null) && (m_Clusterer instanceof OptionHandler))
        options = ((OptionHandler) m_Clusterer).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 (getClusterer() == null) {
        println("\n=== No clusterer set ===");
        return;
      }
      println("\n=== Check on Clusterer: "
          + getClusterer().getClass().getName()
          + " ===\n");
      
      // Start tests
      println("--> Checking for interfaces");
      canTakeOptions();
      boolean updateable = updateableClusterer()[0];
      boolean weightedInstancesHandler = weightedInstancesHandler()[0];
      boolean multiInstanceHandler = multiInstanceHandler()[0];
      println("--> Clusterer tests");
      declaresSerialVersionUID();
      runTests(weightedInstancesHandler, multiInstanceHandler, updateable);
    }
    
    /**
     * Set the clusterer for testing. 
     *
     * @param newClusterer the Clusterer to use.
     */
    public void setClusterer(Clusterer newClusterer) {
      m_Clusterer = newClusterer;
    }
    
    /**
     * Get the clusterer used as the clusterer
     *
     * @return the clusterer used as the clusterer
     */
    public Clusterer getClusterer() {
      return m_Clusterer;
    }
    
    /**
     * Run a battery of tests
     *
     * @param weighted true if the clusterer says it handles weights
     * @param multiInstance true if the clusterer is a multi-instance clusterer
     * @param updateable true if the classifier is updateable
     */
    protected void runTests(boolean weighted, boolean multiInstance, boolean updateable) {
      
      boolean PNom = canPredict(true,  false, false, false, false, multiInstance)[0];
      boolean PNum = canPredict(false, true,  false, false, false, multiInstance)[0];
      boolean PStr = canPredict(false, false, true,  false, false, multiInstance)[0];
      boolean PDat = canPredict(false, false, false, true,  false, multiInstance)[0];
      boolean PRel;
      if (!multiInstance)
        PRel = canPredict(false, false, false, false,  true, multiInstance)[0];
      else
        PRel = false;
  
      if (PNom || PNum || PStr || PDat || PRel) {
        if (weighted)
          instanceWeights(PNom, PNum, PStr, PDat, PRel, multiInstance);
        
        canHandleZeroTraining(PNom, PNum, PStr, PDat, PRel, multiInstance);
        boolean handleMissingPredictors = canHandleMissing(PNom, PNum, PStr, PDat, PRel, 
            multiInstance, true, 20)[0];
        if (handleMissingPredictors)
          canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, true, 100);
        
        correctBuildInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance);
        datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, handleMissingPredictors);
        if (updateable)
          updatingEquality(PNom, PNum, PStr, PDat, PRel, multiInstance);
      }
    }
    
    /**
     * Checks whether the scheme can take command line options.
     *
     * @return index 0 is true if the clusterer can take options
     */
    protected boolean[] canTakeOptions() {
      
      boolean[] result = new boolean[2];
      
      print("options...");
      if (m_Clusterer instanceof OptionHandler) {
        println("yes");
        if (m_Debug) {
          println("\n=== Full report ===");
          Enumeration enu = ((OptionHandler)m_Clusterer).listOptions();
          while (enu.hasMoreElements()) {
            Option option = (Option) enu.nextElement();
            print(option.synopsis() + "\n" 
                + option.description() + "\n");
          }
          println("\n");
        }
        result[0] = true;
      }
      else {
        println("no");
        result[0] = false;
      }
      
      return result;
    }
    
    /**
     * Checks whether the scheme can build models incrementally.
     *
     * @return index 0 is true if the clusterer can train incrementally
     */
    protected boolean[] updateableClusterer() {
      
      boolean[] result = new boolean[2];
      
      print("updateable clusterer...");
      if (m_Clusterer instanceof UpdateableClusterer) {
        println("yes");
        result[0] = true;
      }
      else {
        println("no");
        result[0] = false;
      }
      
      return result;
    }
    
    /**
     * Checks whether the scheme says it can handle instance weights.
     *
     * @return true if the clusterer handles instance weights
     */
    protected boolean[] weightedInstancesHandler() {
      
      boolean[] result = new boolean[2];
      
      print("weighted instances clusterer...");
      if (m_Clusterer 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 clusterer handles multi-instance data
     */
    protected boolean[] multiInstanceHandler() {
      boolean[] result = new boolean[2];
      
      print("multi-instance clusterer...");
      if (m_Clusterer 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_Clusterer.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
     * @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) {
      
      print("basic predict");
      printAttributeSummary(
          nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance);
      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(), missingLevel = 0;
      boolean predictorMissing = false;
      
      return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, 
          datePredictor, relationalPredictor, 
          multiInstance,
          missingLevel, predictorMissing, 
          numTrain, 
          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
     * @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) {
      
      print("handle zero training instances");
      printAttributeSummary(
          nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance);
      print("...");
      FastVector accepts = new FastVector();
      accepts.addElement("train");
      accepts.addElement("value");
      int numTrain = 0, missingLevel = 0;
      boolean predictorMissing = false;
      
      return runBasicTest(
                nominalPredictor, numericPredictor, stringPredictor, 
                datePredictor, relationalPredictor, 
                multiInstance,
                missingLevel, predictorMissing,
                numTrain, 
                accepts);
    }
    
    /**
     * Checks whether the scheme correctly initialises models when 
     * buildClusterer is called. This test calls buildClusterer with
     * one training dataset. buildClusterer is then called on a training set 
     * with different structure, and then again with the original training set. 
     * If the equals method of the ClusterEvaluation 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
     * @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) {
  
      boolean[] result = new boolean[2];
      
      print("correct initialisation during buildClusterer");
      printAttributeSummary(
          nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance);
      print("...");
      int numTrain = getNumInstances(), missingLevel = 0;
      boolean predictorMissing = false;
      
      Instances train1 = null;
      Instances train2 = null;
      Clusterer clusterer = null;
      ClusterEvaluation evaluation1A = null;
      ClusterEvaluation evaluation1B = null;
      ClusterEvaluation evaluation2 = null;
      boolean built = false;
      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, 
                                 multiInstance);
        train2 = makeTestDataset(84, numTrain, 
                                 nominalPredictor    ? getNumNominal() + 1 : 0,
                                 numericPredictor    ? getNumNumeric() + 1 : 0, 
                                 stringPredictor     ? getNumString()      : 0, 
                                 datePredictor       ? getNumDate()        : 0, 
                                 relationalPredictor ? getNumRelational()  : 0, 
                                 multiInstance);
        if (nominalPredictor && !multiInstance) {
          train1.deleteAttributeAt(0);
          train2.deleteAttributeAt(0);
        }
        if (missingLevel > 0) {
          addMissing(train1, missingLevel, predictorMissing);
          addMissing(train2, missingLevel, predictorMissing);
        }
        
        clusterer = AbstractClusterer.makeCopies(getClusterer(), 1)[0];
        evaluation1A = new ClusterEvaluation();
        evaluation1B = new ClusterEvaluation();
        evaluation2 = new ClusterEvaluation();
      } catch (Exception ex) {
        throw new Error("Error setting up for tests: " + ex.getMessage());
      }
      try {
        stage = 0;
        clusterer.buildClusterer(train1);
        built = true;
        evaluation1A.setClusterer(clusterer);
        evaluation1A.evaluateClusterer(train1);
        
        stage = 1;
        built = false;
        clusterer.buildClusterer(train2);
        built = true;
        evaluation2.setClusterer(clusterer);
        evaluation2.evaluateClusterer(train2);
        
        stage = 2;
        built = false;
        clusterer.buildClusterer(train1);
        built = true;
        evaluation1B.setClusterer(clusterer);
        evaluation1B.evaluateClusterer(train1);
        
        stage = 3;
        if (!evaluation1A.equals(evaluation1B)) {
          if (m_Debug) {
            println("\n=== Full report ===\n");
            println("First buildClusterer()");
            println(evaluation1A.clusterResultsToString() + "\n\n");
            println("Second buildClusterer()");
            println(evaluation1B.clusterResultsToString() + "\n\n");
          }
          throw new Exception("Results differ between buildClusterer calls");
        }
        println("yes");
        result[0] = true;
        
        if (false && m_Debug) {
          println("\n=== Full report ===\n");
          println("First buildClusterer()");
          println(evaluation1A.clusterResultsToString() + "\n\n");
          println("Second buildClusterer()");
          println(evaluation1B.clusterResultsToString() + "\n\n");
        }
      } 
      catch (Exception ex) {
        println("no");
        result[0] = false;
        if (m_Debug) {
          println("\n=== Full Report ===");
          print("Problem during");
          if (built) {
            print(" testing");
          } else {
            print(" training");
          }
          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 predictorMissing true if the missing values may be in 
     * the predictors
     * @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,
        boolean predictorMissing,
        int missingLevel) {
      
      if (missingLevel == 100)
        print("100% ");
      print("missing");
      if (predictorMissing) {
        print(" predictor");
      }
      print(" values");
      printAttributeSummary(
          nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance);
      print("...");
      FastVector accepts = new FastVector();
      accepts.addElement("missing");
      accepts.addElement("value");
      accepts.addElement("train");
      int numTrain = getNumInstances();
      
      return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, 
          datePredictor, relationalPredictor, 
          multiInstance,
          missingLevel, predictorMissing,
          numTrain, 
          accepts);
    }
    
    /**
     * Checks whether the clusterer can handle instance weights.
     * This test compares the clusterer performance on two datasets
     * that are identical except for the training weights. If the 
     * results change, then the clusterer 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 clusterer 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
     * @return index 0 true if the test was passed
     */
    protected boolean[] instanceWeights(
        boolean nominalPredictor,
        boolean numericPredictor, 
        boolean stringPredictor, 
        boolean datePredictor,
        boolean relationalPredictor,
        boolean multiInstance) {
      
      print("clusterer uses instance weights");
      printAttributeSummary(
          nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance);
      print("...");
      int numTrain = 2*getNumInstances(), missingLevel = 0;
      boolean predictorMissing = false;
      
      boolean[] result = new boolean[2];
      Instances train = null;
      Clusterer [] clusterers = null;
      ClusterEvaluation evaluationB = null;
      ClusterEvaluation evaluationI = null;
      boolean built = false;
      boolean evalFail = false;
      try {
        train = makeTestDataset(42, numTrain, 
                                nominalPredictor    ? getNumNominal() + 1 : 0,
                                numericPredictor    ? getNumNumeric() + 1 : 0, 
                                stringPredictor     ? getNumString()      : 0, 
                                datePredictor       ? getNumDate()        : 0, 
                                relationalPredictor ? getNumRelational()  : 0, 
                                multiInstance);
        if (nominalPredictor && !multiInstance)
          train.deleteAttributeAt(0);
        if (missingLevel > 0)
          addMissing(train, missingLevel, predictorMissing);
        clusterers = AbstractClusterer.makeCopies(getClusterer(), 2);
        evaluationB = new ClusterEvaluation();
        evaluationI = new ClusterEvaluation();
        clusterers[0].buildClusterer(train);
        evaluationB.setClusterer(clusterers[0]);
      } 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);
        }
        clusterers[1].buildClusterer(train);
        built = true;
        evaluationI.setClusterer(clusterers[1]);
        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("\nboth methods\n");
            println(evaluationB.clusterResultsToString());
          } else {
            print("Problem during");
            if (built) {
              print(" testing");
            } else {
              print(" 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 predictorMissing true if we know the clusterer can handle
     * (at least) moderate missing predictor 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,
        boolean predictorMissing) {
      
      print("clusterer doesn't alter original datasets");
      printAttributeSummary(
          nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance);
      print("...");
      int numTrain = getNumInstances(), missingLevel = 20;
      
      boolean[] result = new boolean[2];
      Instances train = null;
      Clusterer clusterer = null;
      try {
        train = makeTestDataset(42, numTrain, 
                                nominalPredictor    ? getNumNominal()    : 0,
                                numericPredictor    ? getNumNumeric()    : 0, 
                                stringPredictor     ? getNumString()     : 0, 
                                datePredictor       ? getNumDate()       : 0, 
                                relationalPredictor ? getNumRelational() : 0, 
                                multiInstance);
        if (nominalPredictor && !multiInstance)
          train.deleteAttributeAt(0);
        if (missingLevel > 0)
          addMissing(train, missingLevel, predictorMissing);
        clusterer = AbstractClusterer.makeCopies(getClusterer(), 1)[0];
      } catch (Exception ex) {
        throw new Error("Error setting up for tests: " + ex.getMessage());
      }
      try {
        Instances trainCopy = new Instances(train);
        clusterer.buildClusterer(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 is the dataset:\n");
          println("=== Train Dataset ===\n"
              + train.toString() + "\n");
        }
      }
      
      return result;
    }
    
    /**
     * Checks whether an updateable scheme produces the same model when
     * trained incrementally as when batch trained. The model itself
     * cannot be compared, so we compare the evaluation on test data
     * for both models. It is possible to get a false positive on this
     * test (likelihood depends on the classifier).
     *
     * @param nominalPredictor if true use nominal predictor attributes
     * @param numericPredictor if true use numeric predictor attributes
     * @param stringPredictor if true use string predictor attributes
     * @param datePredictor if true use date predictor attributes
     * @param relationalPredictor if true use relational predictor attributes
     * @param multiInstance whether multi-instance is needed
     * @return index 0 is true if the test was passed
     */
    protected boolean[] updatingEquality(
        boolean nominalPredictor,
        boolean numericPredictor, 
        boolean stringPredictor, 
        boolean datePredictor,
        boolean relationalPredictor,
        boolean multiInstance) {
      
      print("incremental training produces the same results"
          + " as batch training");
      printAttributeSummary(
          nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance);
      print("...");
      int numTrain = getNumInstances(), missingLevel = 0;
      boolean predictorMissing = false, classMissing = false;
      
      boolean[] result = new boolean[2];
      Instances train = null;
      Clusterer[] clusterers = null;
      ClusterEvaluation evaluationB = null;
      ClusterEvaluation evaluationI = null;
      boolean built = false;
      try {
        train = makeTestDataset(42, numTrain, 
                                nominalPredictor    ? getNumNominal()    : 0,
                                numericPredictor    ? getNumNumeric()    : 0, 
                                stringPredictor     ? getNumString()     : 0, 
                                datePredictor       ? getNumDate()       : 0, 
                                relationalPredictor ? getNumRelational() : 0, 
                                multiInstance);
        if (missingLevel > 0)
          addMissing(train, missingLevel, predictorMissing, classMissing);
        clusterers = AbstractClusterer.makeCopies(getClusterer(), 2);
        evaluationB = new ClusterEvaluation();
        evaluationI = new ClusterEvaluation();
        clusterers[0].buildClusterer(train);
        evaluationB.setClusterer(clusterers[0]);
      } catch (Exception ex) {
        throw new Error("Error setting up for tests: " + ex.getMessage());
      }
      try {
        clusterers[1].buildClusterer(new Instances(train, 0));
        for (int i = 0; i < train.numInstances(); i++) {
          ((UpdateableClusterer)clusterers[1]).updateClusterer(
              train.instance(i));
        }
        built = true;
        evaluationI.setClusterer(clusterers[1]);
        if (!evaluationB.equals(evaluationI)) {
          println("no");
          result[0] = false;
          
          if (m_Debug) {
            println("\n=== Full Report ===");
            println("Results differ between batch and "
                + "incrementally built models.\n"
                + "Depending on the classifier, this may be OK");
            println("Here are the results:\n");
            println("\nbatch built results\n" + evaluationB.clusterResultsToString());
            println("\nincrementally built results\n" + evaluationI.clusterResultsToString());
            println("Here are the datasets:\n");
            println("=== Train Dataset ===\n"
                + train.toString() + "\n");
          }
        }
        else {
          println("yes");
          result[0] = true;
        }
      } catch (Exception ex) {
        result[0] = false;
        
        print("Problem during");
        if (built)
          print(" testing");
        else
          print(" training");
        println(": " + ex.getMessage() + "\n");
      }
      
      return result;
    }
    
    /**
     * 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 missingLevel the percentage of missing values
     * @param predictorMissing true if the missing values may be in 
     * the predictors
     * @param numTrain the number of instances in the training set
     * @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 missingLevel,
        boolean predictorMissing,
        int numTrain,
        FastVector accepts) {
      
      boolean[] result = new boolean[2];
      Instances train = null;
      Clusterer clusterer = null;
      try {
        train = makeTestDataset(42, numTrain, 
                                nominalPredictor    ? getNumNominal()    : 0,
                                numericPredictor    ? getNumNumeric()    : 0, 
                                stringPredictor     ? getNumString()     : 0,
                                datePredictor       ? getNumDate()       : 0,
                                relationalPredictor ? getNumRelational() : 0,
                                multiInstance);
        if (nominalPredictor && !multiInstance)
          train.deleteAttributeAt(0);
        if (missingLevel > 0)
          addMissing(train, missingLevel, predictorMissing);
        clusterer = AbstractClusterer.makeCopies(getClusterer(), 1)[0];
      } catch (Exception ex) {
        ex.printStackTrace();
        throw new Error("Error setting up for tests: " + ex.getMessage());
      }
      try {
        clusterer.buildClusterer(train);
        println("yes");
        result[0] = true;
      } 
      catch (Exception ex) {
        boolean acceptable = false;
        String msg = ex.getMessage().toLowerCase();
        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;
    }
    
    /**
     * Add missing values to a dataset.
     *
     * @param data the instances to add missing values to
     * @param level the level of missing values to add (if positive, this
     * is the probability that a value will be set to missing, if negative
     * all but one value will be set to missing (not yet implemented))
     * @param predictorMissing if true, predictor attributes will be modified
     */
    protected void addMissing(Instances data, int level, boolean predictorMissing) {
      
      Random random = new Random(1);
      for (int i = 0; i < data.numInstances(); i++) {
        Instance current = data.instance(i);
        for (int j = 0; j < data.numAttributes(); j++) {
          if (predictorMissing) {
            if (Math.abs(random.nextInt()) % 100 < level)
              current.setMissing(j);
          }
        }
      }
    }
    
    /**
     * 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 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
     */
    protected Instances makeTestDataset(int seed, int numInstances, 
                                        int numNominal, int numNumeric, 
                                        int numString, int numDate,
                                        int numRelational,
                                        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.setClassIndex(TestInstances.NO_CLASS);
      dataset.setMultiInstance(multiInstance);
      
      return 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
     */
    protected void printAttributeSummary(boolean nominalPredictor, 
                                         boolean numericPredictor, 
                                         boolean stringPredictor, 
                                         boolean datePredictor, 
                                         boolean relationalPredictor, 
                                         boolean multiInstance) {
      
      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 = " (" + str + " predictors)";
      
      print(str);
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.11 $");
    }
    
    /**
     * Test method for this class
     * 
     * @param args the commandline options
     */
    public static void main(String [] args) {
      runCheck(new CheckClusterer(), args);
    }
  }
  
  