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.
   */
  
  /*
   *    RandomSplitResultProducer.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  
  package weka.experiment;
  
  import weka.core.AdditionalMeasureProducer;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.OptionHandler;
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  
  import java.io.File;
  import java.util.Calendar;
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.TimeZone;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -P &lt;percent&gt;
   *  The percentage of instances to use for training.
   *  (default 66)</pre>
   * 
   * <pre> -D
   * Save raw split evaluator output.</pre>
   * 
   * <pre> -O &lt;file/directory name/path&gt;
   *  The filename where raw output will be stored.
   *  If a directory name is specified then then individual
   *  outputs will be gzipped, otherwise all output will be
   *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)</pre>
   * 
   * <pre> -W &lt;class name&gt;
   *  The full class name of a SplitEvaluator.
   *  eg: weka.experiment.ClassifierSplitEvaluator</pre>
   * 
   * <pre> -R
   *  Set when data is not to be randomized and the data sets' size.
   *  Is not to be determined via probabilistic rounding.</pre>
   * 
   * <pre> 
   * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
   * </pre>
   * 
   * <pre> -W &lt;class name&gt;
   *  The full class name of the classifier.
   *  eg: weka.classifiers.bayes.NaiveBayes</pre>
   * 
   * <pre> -C &lt;index&gt;
   *  The index of the class for which IR statistics
   *  are to be output. (default 1)</pre>
   * 
   * <pre> -I &lt;index&gt;
   *  The index of an attribute to output in the
   *  results. This attribute should identify an
   *  instance in order to know which instances are
   *  in the test set of a cross validation. if 0
   *  no output (default 0).</pre>
   * 
   * <pre> -P
   *  Add target and prediction columns to the result
   *  for each fold.</pre>
   * 
   * <pre> 
   * Options specific to classifier weka.classifiers.rules.ZeroR:
   * </pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   <!-- options-end -->
   * 
   * All options after -- will be passed to the split evaluator.
   *
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @version $Revision: 6256 $
   */
  public class RandomSplitResultProducer 
    implements ResultProducer, OptionHandler, AdditionalMeasureProducer, 
               RevisionHandler {
    
    /** for serialization */
    static final long serialVersionUID = 1403798165056795073L;
    
    /** The dataset of interest */
    protected Instances m_Instances;
  
    /** The ResultListener to send results to */
    protected ResultListener m_ResultListener = new CSVResultListener();
  
    /** The percentage of instances to use for training */
    protected double m_TrainPercent = 66;
  
    /** Whether dataset is to be randomized */
    protected boolean m_randomize = true;
  
    /** The SplitEvaluator used to generate results */
    protected SplitEvaluator m_SplitEvaluator = new ClassifierSplitEvaluator();
  
    /** The names of any additional measures to look for in SplitEvaluators */
    protected String [] m_AdditionalMeasures = null;
  
    /** Save raw output of split evaluators --- for debugging purposes */
    protected boolean m_debugOutput = false;
  
    /** The output zipper to use for saving raw splitEvaluator output */
    protected OutputZipper m_ZipDest = null;
  
    /** The destination output file/directory for raw output */
    protected File m_OutputFile = new File(
  			        new File(System.getProperty("user.dir")), 
  				"splitEvalutorOut.zip");
  
    /** The name of the key field containing the dataset name */
    public static String DATASET_FIELD_NAME = "Dataset";
  
    /** The name of the key field containing the run number */
    public static String RUN_FIELD_NAME = "Run";
  
    /** The name of the result field containing the timestamp */
    public static String TIMESTAMP_FIELD_NAME = "Date_time";
  
    /**
     * Returns a string describing this result producer
     * @return a description of the result producer suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return
          "Generates a single train/test split and calls the appropriate "
        + "SplitEvaluator to generate some results.";
    }
  
    /**
     * Sets the dataset that results will be obtained for.
     *
     * @param instances a value of type 'Instances'.
     */
    public void setInstances(Instances instances) {
      
      m_Instances = instances;
    }
  
    /**
     * Set a list of method names for additional measures to look for
     * in SplitEvaluators. This could contain many measures (of which only a
     * subset may be produceable by the current SplitEvaluator) if an experiment
     * is the type that iterates over a set of properties.
     * @param additionalMeasures an array of measure names, null if none
     */
    public void setAdditionalMeasures(String [] additionalMeasures) {
      m_AdditionalMeasures = additionalMeasures;
  
      if (m_SplitEvaluator != null) {
        System.err.println("RandomSplitResultProducer: setting additional "
  			 +"measures for "
  			 +"split evaluator");
        m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
      }
    }
    
      /**
       * Returns an enumeration of any additional measure names that might be
     * in the SplitEvaluator
     * @return an enumeration of the measure names
     */
    public Enumeration enumerateMeasures() {
      Vector newVector = new Vector();
      if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
        Enumeration en = ((AdditionalMeasureProducer)m_SplitEvaluator).
  	enumerateMeasures();
        while (en.hasMoreElements()) {
  	String mname = (String)en.nextElement();
  	newVector.addElement(mname);
        }
      }
      return newVector.elements();
    }
    
    /**
     * Returns the value of the named measure
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String additionalMeasureName) {
      if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
        return ((AdditionalMeasureProducer)m_SplitEvaluator).
  	getMeasure(additionalMeasureName);
      } else {
        throw new IllegalArgumentException("RandomSplitResultProducer: "
  			  +"Can't return value for : "+additionalMeasureName
  			  +". "+m_SplitEvaluator.getClass().getName()+" "
  			  +"is not an AdditionalMeasureProducer");
      }
    }
    
    /**
     * Sets the object to send results of each run to.
     *
     * @param listener a value of type 'ResultListener'
     */
    public void setResultListener(ResultListener listener) {
  
      m_ResultListener = listener;
    }
  
    /**
     * Gets a Double representing the current date and time.
     * eg: 1:46pm on 20/5/1999 -> 19990520.1346
     *
     * @return a value of type Double
     */
    public static Double getTimestamp() {
  
      Calendar now = Calendar.getInstance(TimeZone.getTimeZone("UTC"));
      double timestamp = now.get(Calendar.YEAR) * 10000
        + (now.get(Calendar.MONTH) + 1) * 100
        + now.get(Calendar.DAY_OF_MONTH)
        + now.get(Calendar.HOUR_OF_DAY) / 100.0
        + now.get(Calendar.MINUTE) / 10000.0;
      return new Double(timestamp);
    }
  
    /**
     * Prepare to generate results.
     *
     * @throws Exception if an error occurs during preprocessing.
     */
    public void preProcess() throws Exception {
  
      if (m_SplitEvaluator == null) {
        throw new Exception("No SplitEvalutor set");
      }
      if (m_ResultListener == null) {
        throw new Exception("No ResultListener set");
      }
      m_ResultListener.preProcess(this);
    }
    
    /**
     * Perform any postprocessing. When this method is called, it indicates
     * that no more requests to generate results for the current experiment
     * will be sent.
     *
     * @throws Exception if an error occurs
     */
    public void postProcess() throws Exception {
  
      m_ResultListener.postProcess(this);
      if (m_debugOutput) {
        if (m_ZipDest != null) {
  	m_ZipDest.finished();
  	m_ZipDest = null;
        }
      }
    }
  
    /**
     * Gets the keys for a specified run number. Different run
     * numbers correspond to different randomizations of the data. Keys
     * produced should be sent to the current ResultListener
     *
     * @param run the run number to get keys for.
     * @throws Exception if a problem occurs while getting the keys
     */
    public void doRunKeys(int run) throws Exception {
      if (m_Instances == null) {
        throw new Exception("No Instances set");
      }
      // Add in some fields to the key like run number, dataset name
      Object [] seKey = m_SplitEvaluator.getKey();
      Object [] key = new Object [seKey.length + 2];
      key[0] = Utils.backQuoteChars(m_Instances.relationName());
      key[1] = "" + run;
      System.arraycopy(seKey, 0, key, 2, seKey.length);
      if (m_ResultListener.isResultRequired(this, key)) {
        try {
  	m_ResultListener.acceptResult(this, key, null);
        } catch (Exception ex) {
  	// Save the train and test datasets for debugging purposes?
  	throw ex;
        }
      }
    }
  
    /**
     * Gets the results for a specified run number. Different run
     * numbers correspond to different randomizations of the data. Results
     * produced should be sent to the current ResultListener
     *
     * @param run the run number to get results for.
     * @throws Exception if a problem occurs while getting the results
     */
    public void doRun(int run) throws Exception {
  
      if (getRawOutput()) {
        if (m_ZipDest == null) {
  	m_ZipDest = new OutputZipper(m_OutputFile);
        }
      }
  
      if (m_Instances == null) {
        throw new Exception("No Instances set");
      }
      // Add in some fields to the key like run number, dataset name
      Object [] seKey = m_SplitEvaluator.getKey();
      Object [] key = new Object [seKey.length + 2];
      key[0] = Utils.backQuoteChars(m_Instances.relationName());
      key[1] = "" + run;
      System.arraycopy(seKey, 0, key, 2, seKey.length);
      if (m_ResultListener.isResultRequired(this, key)) {
  
        // Randomize on a copy of the original dataset
        Instances runInstances = new Instances(m_Instances);
  
        Instances train;
        Instances test;
  
        if (!m_randomize) {
  
  	// Don't do any randomization
  	int trainSize = Utils.round(runInstances.numInstances() * m_TrainPercent / 100);
  	int testSize = runInstances.numInstances() - trainSize;
  	train = new Instances(runInstances, 0, trainSize);
  	test = new Instances(runInstances, trainSize, testSize);
        } else {
  	Random rand = new Random(run);
  	runInstances.randomize(rand);
  	
  	// Nominal class
  	if (runInstances.classAttribute().isNominal()) {
  	  
  	  // create the subset for each classs
  	  int numClasses = runInstances.numClasses();
  	  Instances[] subsets = new Instances[numClasses + 1];
  	  for (int i=0; i < numClasses + 1; i++) {
  	    subsets[i] = new Instances(runInstances, 10);
  	  }
  	  
  	  // divide instances into subsets
  	  Enumeration e = runInstances.enumerateInstances();
  	  while(e.hasMoreElements()) {
  	    Instance inst = (Instance) e.nextElement();
  	    if (inst.classIsMissing()) {
  	      subsets[numClasses].add(inst);
  	    } else {
  	      subsets[(int) inst.classValue()].add(inst);
  	    }
  	  }
  	  
  	  // Compactify them
  	  for (int i=0; i < numClasses + 1; i++) {
  	    subsets[i].compactify();
  	  }
  	  
  	  // merge into train and test sets
  	  train = new Instances(runInstances, runInstances.numInstances());
  	  test = new Instances(runInstances, runInstances.numInstances());
  	  for (int i = 0; i < numClasses + 1; i++) {
  	    int trainSize = 
  	      Utils.probRound(subsets[i].numInstances() * m_TrainPercent / 100, rand);
  	    for (int j = 0; j < trainSize; j++) {
  	      train.add(subsets[i].instance(j));
  	    }
  	    for (int j = trainSize; j < subsets[i].numInstances(); j++) {
  	      test.add(subsets[i].instance(j));
  	    }
  	    // free memory
  	    subsets[i] = null;
  	  }
  	  train.compactify();
  	  test.compactify();
  	  
  	  // randomize the final sets
  	  train.randomize(rand);
  	  test.randomize(rand);
  	} else {
  	  
  	  // Numeric target 
  	  int trainSize = 
  	    Utils.probRound(runInstances.numInstances() * m_TrainPercent / 100, rand);
  	  int testSize = runInstances.numInstances() - trainSize;
  	  train = new Instances(runInstances, 0, trainSize);
  	  test = new Instances(runInstances, trainSize, testSize);
  	}
        }
        try {
  	Object [] seResults = m_SplitEvaluator.getResult(train, test);
  	Object [] results = new Object [seResults.length + 1];
  	results[0] = getTimestamp();
  	System.arraycopy(seResults, 0, results, 1,
  			 seResults.length);
  	if (m_debugOutput) {
  	  String resultName = 
  	    (""+run+"."+
  	     Utils.backQuoteChars(runInstances.relationName())
  	     +"."
  	     +m_SplitEvaluator.toString()).replace(' ','_');
  	  resultName = Utils.removeSubstring(resultName, 
  					     "weka.classifiers.");
  	  resultName = Utils.removeSubstring(resultName, 
  					     "weka.filters.");
  	  resultName = Utils.removeSubstring(resultName, 
  					     "weka.attributeSelection.");
  	  m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
  	}
  	m_ResultListener.acceptResult(this, key, results);
        } catch (Exception ex) {
  	// Save the train and test datasets for debugging purposes?
  	throw ex;
        }
      }
    }
  
    /**
     * Gets the names of each of the columns produced for a single run.
     * This method should really be static.
     *
     * @return an array containing the name of each column
     */
    public String [] getKeyNames() {
  
      String [] keyNames = m_SplitEvaluator.getKeyNames();
      // Add in the names of our extra key fields
      String [] newKeyNames = new String [keyNames.length + 2];
      newKeyNames[0] = DATASET_FIELD_NAME;
      newKeyNames[1] = RUN_FIELD_NAME;
      System.arraycopy(keyNames, 0, newKeyNames, 2, keyNames.length);
      return newKeyNames;
    }
  
    /**
     * Gets the data types of each of the columns produced for a single run.
     * This method should really be static.
     *
     * @return an array containing objects of the type of each column. The 
     * objects should be Strings, or Doubles.
     */
    public Object [] getKeyTypes() {
  
      Object [] keyTypes = m_SplitEvaluator.getKeyTypes();
      // Add in the types of our extra fields
      Object [] newKeyTypes = new String [keyTypes.length + 2];
      newKeyTypes[0] = new String();
      newKeyTypes[1] = new String();
      System.arraycopy(keyTypes, 0, newKeyTypes, 2, keyTypes.length);
      return newKeyTypes;
    }
  
    /**
     * Gets the names of each of the columns produced for a single run.
     * This method should really be static.
     *
     * @return an array containing the name of each column
     */
    public String [] getResultNames() {
  
      String [] resultNames = m_SplitEvaluator.getResultNames();
      // Add in the names of our extra Result fields
      String [] newResultNames = new String [resultNames.length + 1];
      newResultNames[0] = TIMESTAMP_FIELD_NAME;
      System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length);
      return newResultNames;
    }
  
    /**
     * Gets the data types of each of the columns produced for a single run.
     * This method should really be static.
     *
     * @return an array containing objects of the type of each column. The 
     * objects should be Strings, or Doubles.
     */
    public Object [] getResultTypes() {
  
      Object [] resultTypes = m_SplitEvaluator.getResultTypes();
      // Add in the types of our extra Result fields
      Object [] newResultTypes = new Object [resultTypes.length + 1];
      newResultTypes[0] = new Double(0);
      System.arraycopy(resultTypes, 0, newResultTypes, 1, resultTypes.length);
      return newResultTypes;
    }
  
    /**
     * Gets a description of the internal settings of the result
     * producer, sufficient for distinguishing a ResultProducer
     * instance from another with different settings (ignoring
     * those settings set through this interface). For example,
     * a cross-validation ResultProducer may have a setting for the
     * number of folds. For a given state, the results produced should
     * be compatible. Typically if a ResultProducer is an OptionHandler,
     * this string will represent the command line arguments required
     * to set the ResultProducer to that state.
     *
     * @return the description of the ResultProducer state, or null
     * if no state is defined
     */
    public String getCompatibilityState() {
  
      String result = "-P " + m_TrainPercent;
      if (!getRandomizeData()) {
        result += " -R";
      }
      if (m_SplitEvaluator == null) {
        result += " <null SplitEvaluator>";
      } else {
        result += " -W " + m_SplitEvaluator.getClass().getName();
      }
      return result + " --";
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String outputFileTipText() {
      return "Set the destination for saving raw output. If the rawOutput "
        +"option is selected, then output from the splitEvaluator for "
        +"individual train-test splits is saved. If the destination is a "
        +"directory, "
        +"then each output is saved to an individual gzip file; if the "
        +"destination is a file, then each output is saved as an entry "
        +"in a zip file.";
    }
  
    /**
     * Get the value of OutputFile.
     *
     * @return Value of OutputFile.
     */
    public File getOutputFile() {
      
      return m_OutputFile;
    }
    
    /**
     * Set the value of OutputFile.
     *
     * @param newOutputFile Value to assign to OutputFile.
     */
    public void setOutputFile(File newOutputFile) {
      
      m_OutputFile = newOutputFile;
    }  
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String randomizeDataTipText() {
      return "Do not randomize dataset and do not perform probabilistic rounding " +
        "if false";
    }
  
    /**
     * Get if dataset is to be randomized
     * @return true if dataset is to be randomized
     */
    public boolean getRandomizeData() {
      return m_randomize;
    }
    
    /**
     * Set to true if dataset is to be randomized
     * @param d true if dataset is to be randomized
     */
    public void setRandomizeData(boolean d) {
      m_randomize = d;
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String rawOutputTipText() {
      return "Save raw output (useful for debugging). If set, then output is "
        +"sent to the destination specified by outputFile";
    }
  
    /**
     * Get if raw split evaluator output is to be saved
     * @return true if raw split evalutor output is to be saved
     */
    public boolean getRawOutput() {
      return m_debugOutput;
    }
    
    /**
     * Set to true if raw split evaluator output is to be saved
     * @param d true if output is to be saved
     */
    public void setRawOutput(boolean d) {
      m_debugOutput = d;
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String trainPercentTipText() {
      return "Set the percentage of data to use for training.";
    }
  
    /**
     * Get the value of TrainPercent.
     *
     * @return Value of TrainPercent.
     */
    public double getTrainPercent() {
      
      return m_TrainPercent;
    }
    
    /**
     * Set the value of TrainPercent.
     *
     * @param newTrainPercent Value to assign to TrainPercent.
     */
    public void setTrainPercent(double newTrainPercent) {
      
      m_TrainPercent = newTrainPercent;
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String splitEvaluatorTipText() {
      return "The evaluator to apply to the test data. "
        +"This may be a classifier, regression scheme etc.";
    }
  
    /**
     * Get the SplitEvaluator.
     *
     * @return the SplitEvaluator.
     */
    public SplitEvaluator getSplitEvaluator() {
      
      return m_SplitEvaluator;
    }
    
    /**
     * Set the SplitEvaluator.
     *
     * @param newSplitEvaluator new SplitEvaluator to use.
     */
    public void setSplitEvaluator(SplitEvaluator newSplitEvaluator) {
      
      m_SplitEvaluator = newSplitEvaluator;
      m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
    }
  
    /**
     * Returns an enumeration describing the available options..
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
  
      Vector newVector = new Vector(5);
  
      newVector.addElement(new Option(
  	     "\tThe percentage of instances to use for training.\n"
  	      +"\t(default 66)", 
  	     "P", 1, 
  	     "-P <percent>"));
  
      newVector.addElement(new Option(
  	     "Save raw split evaluator output.",
  	     "D",0,"-D"));
  
      newVector.addElement(new Option(
  	     "\tThe filename where raw output will be stored.\n"
  	     +"\tIf a directory name is specified then then individual\n"
  	     +"\toutputs will be gzipped, otherwise all output will be\n"
  	     +"\tzipped to the named file. Use in conjuction with -D."
  	     +"\t(default splitEvalutorOut.zip)", 
  	     "O", 1, 
  	     "-O <file/directory name/path>"));
  
      newVector.addElement(new Option(
  	     "\tThe full class name of a SplitEvaluator.\n"
  	      +"\teg: weka.experiment.ClassifierSplitEvaluator", 
  	     "W", 1, 
  	     "-W <class name>"));
  
      newVector.addElement(new Option(
  	     "\tSet when data is not to be randomized and the data sets' size.\n"
  	     + "\tIs not to be determined via probabilistic rounding.",
  	     "R",0,"-R"));
  
   
      if ((m_SplitEvaluator != null) &&
  	(m_SplitEvaluator instanceof OptionHandler)) {
        newVector.addElement(new Option(
  	     "",
  	     "", 0, "\nOptions specific to split evaluator "
  	     + m_SplitEvaluator.getClass().getName() + ":"));
        Enumeration enu = ((OptionHandler)m_SplitEvaluator).listOptions();
        while (enu.hasMoreElements()) {
  	newVector.addElement(enu.nextElement());
        }
      }
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -P &lt;percent&gt;
     *  The percentage of instances to use for training.
     *  (default 66)</pre>
     * 
     * <pre> -D
     * Save raw split evaluator output.</pre>
     * 
     * <pre> -O &lt;file/directory name/path&gt;
     *  The filename where raw output will be stored.
     *  If a directory name is specified then then individual
     *  outputs will be gzipped, otherwise all output will be
     *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)</pre>
     * 
     * <pre> -W &lt;class name&gt;
     *  The full class name of a SplitEvaluator.
     *  eg: weka.experiment.ClassifierSplitEvaluator</pre>
     * 
     * <pre> -R
     *  Set when data is not to be randomized and the data sets' size.
     *  Is not to be determined via probabilistic rounding.</pre>
     * 
     * <pre> 
     * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
     * </pre>
     * 
     * <pre> -W &lt;class name&gt;
     *  The full class name of the classifier.
     *  eg: weka.classifiers.bayes.NaiveBayes</pre>
     * 
     * <pre> -C &lt;index&gt;
     *  The index of the class for which IR statistics
     *  are to be output. (default 1)</pre>
     * 
     * <pre> -I &lt;index&gt;
     *  The index of an attribute to output in the
     *  results. This attribute should identify an
     *  instance in order to know which instances are
     *  in the test set of a cross validation. if 0
     *  no output (default 0).</pre>
     * 
     * <pre> -P
     *  Add target and prediction columns to the result
     *  for each fold.</pre>
     * 
     * <pre> 
     * Options specific to classifier weka.classifiers.rules.ZeroR:
     * </pre>
     * 
     * <pre> -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console</pre>
     * 
     <!-- options-end -->
     *
     * All options after -- will be passed to the split evaluator.
     *
     * @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 {
      
      setRawOutput(Utils.getFlag('D', options));
      setRandomizeData(!Utils.getFlag('R', options));
  
      String fName = Utils.getOption('O', options);
      if (fName.length() != 0) {
        setOutputFile(new File(fName));
      }
  
      String trainPct = Utils.getOption('P', options);
      if (trainPct.length() != 0) {
        setTrainPercent((new Double(trainPct)).doubleValue());
      } else {
        setTrainPercent(66);
      }
  
      String seName = Utils.getOption('W', options);
      if (seName.length() == 0) {
        throw new Exception("A SplitEvaluator must be specified with"
  			  + " the -W option.");
      }
      // Do it first without options, so if an exception is thrown during
      // the option setting, listOptions will contain options for the actual
      // SE.
      setSplitEvaluator((SplitEvaluator)Utils.forName(
  		      SplitEvaluator.class,
  		      seName,
  		      null));
      if (getSplitEvaluator() instanceof OptionHandler) {
        ((OptionHandler) getSplitEvaluator())
  	.setOptions(Utils.partitionOptions(options));
      }
    }
  
    /**
     * Gets the current settings of the result producer.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String [] getOptions() {
  
      String [] seOptions = new String [0];
      if ((m_SplitEvaluator != null) && 
  	(m_SplitEvaluator instanceof OptionHandler)) {
        seOptions = ((OptionHandler)m_SplitEvaluator).getOptions();
      }
      
      String [] options = new String [seOptions.length + 9];
      int current = 0;
  
      options[current++] = "-P"; options[current++] = "" + getTrainPercent();
      
      if (getRawOutput()) {
        options[current++] = "-D";
      }
      
      if (!getRandomizeData()) {
        options[current++] = "-R";
      }
  
      options[current++] = "-O"; 
      options[current++] = getOutputFile().getName();
  
      if (getSplitEvaluator() != null) {
        options[current++] = "-W";
        options[current++] = getSplitEvaluator().getClass().getName();
      }
      options[current++] = "--";
  
      System.arraycopy(seOptions, 0, options, current, 
  		     seOptions.length);
      current += seOptions.length;
      while (current < options.length) {
        options[current++] = "";
      }
      return options;
    }
  
    /**
     * Gets a text descrption of the result producer.
     *
     * @return a text description of the result producer.
     */
    public String toString() {
  
      String result = "RandomSplitResultProducer: ";
      result += getCompatibilityState();
      if (m_Instances == null) {
        result += ": <null Instances>";
      } else {
        result += ": " + Utils.backQuoteChars(m_Instances.relationName());
      }
      return result;
    }
  
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 6256 $");
    }
  } // RandomSplitResultProducer
  