/*
 *    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 java.io.File;
import java.util.Calendar;
import java.util.Enumeration;
import java.util.Random;
import java.util.TimeZone;
import java.util.Vector;

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;

/**
 * <!-- 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: 11198 $
 */
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'.
   */
  @Override
  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
   */
  @Override
  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
   */
  @Override
  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
   */
  @Override
  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'
   */
  @Override
  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.
   */
  @Override
  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
   */
  @Override
  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
   */
  @Override
  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
   */
  @Override
  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
   */
  @Override
  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.
   */
  @Override
  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
   */
  @Override
  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.
   */
  @Override
  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
   */
  @Override
  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.
   */
  @Override
  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
   */
  @Override
  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) {

      // 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
   */
  @Override
  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.
   */
  @Override
  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
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 11198 $");
  }
} // RandomSplitResultProducer
