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.
   */
  
  /*
   *    StratifiedRemoveFolds.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  
  package weka.filters.supervised.instance;
  
  import weka.core.Capabilities;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.OptionHandler;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  import weka.core.Capabilities.Capability;
  import weka.filters.Filter;
  import weka.filters.SupervisedFilter;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * This filter takes a dataset and outputs a specified fold for cross validation. If you do not want the folds to be stratified use the unsupervised version.
   * <p/>
   <!-- globalinfo-end -->
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -V
   *  Specifies if inverse of selection is to be output.
   * </pre>
   * 
   * <pre> -N &lt;number of folds&gt;
   *  Specifies number of folds dataset is split into. 
   *  (default 10)
   * </pre>
   * 
   * <pre> -F &lt;fold&gt;
   *  Specifies which fold is selected. (default 1)
   * </pre>
   * 
   * <pre> -S &lt;seed&gt;
   *  Specifies random number seed. (default 0, no randomizing)
   * </pre>
   * 
   <!-- options-end -->
   *
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @version $Revision: 5492 $ 
   */
  public class StratifiedRemoveFolds 
    extends Filter 
    implements SupervisedFilter, OptionHandler {
    
    /** for serialization */
    static final long serialVersionUID = -7069148179905814324L;
  
    /** Indicates if inverse of selection is to be output. */
    private boolean m_Inverse = false;
  
    /** Number of folds to split dataset into */
    private int m_NumFolds = 10;
  
    /** Fold to output */
    private int m_Fold = 1;
  
    /** Random number seed. */
    private long m_Seed = 0;
  
    /**
     * Gets an enumeration describing the available options..
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
  
      Vector newVector = new Vector(6);
  
      newVector.addElement(new Option(
  	      "\tSpecifies if inverse of selection is to be output.\n",
  	      "V", 0, "-V"));
  
      newVector.addElement(new Option(
                "\tSpecifies number of folds dataset is split into. \n"
  	      + "\t(default 10)\n",
                "N", 1, "-N <number of folds>"));
  
      newVector.addElement(new Option(
  	      "\tSpecifies which fold is selected. (default 1)\n",
  	      "F", 1, "-F <fold>"));
  
      newVector.addElement(new Option(
  	      "\tSpecifies random number seed. (default 0, no randomizing)\n",
  	      "S", 1, "-S <seed>"));
  
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. <p/>
     * 
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -V
     *  Specifies if inverse of selection is to be output.
     * </pre>
     * 
     * <pre> -N &lt;number of folds&gt;
     *  Specifies number of folds dataset is split into. 
     *  (default 10)
     * </pre>
     * 
     * <pre> -F &lt;fold&gt;
     *  Specifies which fold is selected. (default 1)
     * </pre>
     * 
     * <pre> -S &lt;seed&gt;
     *  Specifies random number seed. (default 0, no randomizing)
     * </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 {
  
      setInvertSelection(Utils.getFlag('V', options));
      String numFolds = Utils.getOption('N', options);
      if (numFolds.length() != 0) {
        setNumFolds(Integer.parseInt(numFolds));
      } else {
        setNumFolds(10);
      }
      String fold = Utils.getOption('F', options);
      if (fold.length() != 0) {
        setFold(Integer.parseInt(fold));
      } else {
        setFold(1);
      }
      String seed = Utils.getOption('S', options);
      if (seed.length() != 0) {
        setSeed(Integer.parseInt(seed));
      } else {
        setSeed(0);
      }
      if (getInputFormat() != null) {
        setInputFormat(getInputFormat());
      }
    }
  
    /**
     * Gets the current settings of the filter.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String [] getOptions() {
  
      String [] options = new String [8];
      int current = 0;
  
      options[current++] = "-S"; options[current++] = "" + getSeed();
      if (getInvertSelection()) {
        options[current++] = "-V";
      }
      options[current++] = "-N"; options[current++] = "" + getNumFolds();
      options[current++] = "-F"; options[current++] = "" + getFold();
      while (current < options.length) {
        options[current++] = "";
      }
      return options;
    }
  
    /**
     * Returns a string describing this filter
     *
     * @return a description of the filter suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return 
          "This filter takes a dataset and outputs a specified fold for "
        + "cross validation. If you do not want the folds to be stratified "
        + "use the unsupervised version.";
    }
  
    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String invertSelectionTipText() {
  
      return "Whether to invert the selection.";
    }
  
    /**
     * Gets if selection is to be inverted.
     *
     * @return true if the selection is to be inverted
     */
    public boolean getInvertSelection() {
  
      return m_Inverse;
    }
  
    /**
     * Sets if selection is to be inverted.
     *
     * @param inverse true if inversion is to be performed
     */
    public void setInvertSelection(boolean inverse) {
      
      m_Inverse = inverse;
    }
  
    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numFoldsTipText() {
  
      return "The number of folds to split the dataset into.";
    }
  
    /**
     * Gets the number of folds in which dataset is to be split into.
     * 
     * @return the number of folds the dataset is to be split into.
     */
    public int getNumFolds() {
  
      return m_NumFolds;
    }
  
    /**
     * Sets the number of folds the dataset is split into. If the number
     * of folds is zero, it won't split it into folds. 
     *
     * @param numFolds number of folds dataset is to be split into
     * @throws IllegalArgumentException if number of folds is negative
     */
    public void setNumFolds(int numFolds) {
  
      if (numFolds < 0) {
        throw new IllegalArgumentException("Number of folds has to be positive or zero.");
      }
      m_NumFolds = numFolds;
    }
  
    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String foldTipText() {
  
      return "The fold which is selected.";
    }
  
    /**
     * Gets the fold which is selected.
     *
     * @return the fold which is selected
     */
    public int getFold() {
  
      return m_Fold;
    }
  
    /**
     * Selects a fold.
     *
     * @param fold the fold to be selected.
     * @throws IllegalArgumentException if fold's index is smaller than 1
     */
    public void setFold(int fold) {
  
      if (fold < 1) {
        throw new IllegalArgumentException("Fold's index has to be greater than 0.");
      }
      m_Fold = fold;
    }
  
    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String seedTipText() {
  
      return "the random number seed for shuffling the dataset. If seed is negative, shuffling will not be performed.";
    }
  
    /**
     * Gets the random number seed used for shuffling the dataset.
     *
     * @return the random number seed
     */
    public long getSeed() {
  
      return m_Seed;
    }
  
    /**
     * Sets the random number seed for shuffling the dataset. If seed
     * is negative, shuffling won't be performed.
     *
     * @param seed the random number seed
     */
    public void setSeed(long seed) {
      
      m_Seed = seed;
    }
  
    /** 
     * Returns the Capabilities of this filter.
     *
     * @return            the capabilities of this object
     * @see               Capabilities
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
  
      // attributes
      result.enableAllAttributes();
      result.enable(Capability.MISSING_VALUES);
      
      // class
      result.enableAllClasses();
      result.enable(Capability.MISSING_CLASS_VALUES);
      
      return result;
    }
  
    /**
     * Sets the format of the input instances.
     *
     * @param instanceInfo an Instances object containing the input instance
     * structure (any instances contained in the object are ignored - only the
     * structure is required).
     * @return true because outputFormat can be collected immediately
     * @throws Exception if the input format can't be set successfully
     */  
    public boolean setInputFormat(Instances instanceInfo) throws Exception {
  
      if ((m_NumFolds > 0) && (m_NumFolds < m_Fold)) {
        throw new IllegalArgumentException("Fold has to be smaller or equal to "+
                                           "number of folds.");
      }
      super.setInputFormat(instanceInfo);
      setOutputFormat(instanceInfo);
      return true;
    }
  
    /**
     * Input an instance for filtering. Filter requires all
     * training instances be read before producing output.
     *
     * @param instance the input instance
     * @return true if the filtered instance may now be
     * collected with output().
     * @throws IllegalStateException if no input structure has been defined
     */
    public boolean input(Instance instance) {
  
      if (getInputFormat() == null) {
        throw new IllegalStateException("No input instance format defined");
      }
      if (m_NewBatch) {
        resetQueue();
        m_NewBatch = false;
      }
      if (isFirstBatchDone()) {
        push(instance);
        return true;
      } else {
        bufferInput(instance);
        return false;
      }
    }
  
    /**
     * Signify that this batch of input to the filter is
     * finished. Output() may now be called to retrieve the filtered
     * instances.
     *
     * @return true if there are instances pending output
     * @throws IllegalStateException if no input structure has been defined 
     */
    public boolean batchFinished() {
  
      if (getInputFormat() == null) {
        throw new IllegalStateException("No input instance format defined");
      }
      
      Instances instances;
  
      if (!isFirstBatchDone()) {
        if (m_Seed > 0) {
  	// User has provided a random number seed.
  	getInputFormat().randomize(new Random(m_Seed));
        }
  
        // Select out a fold
        getInputFormat().stratify(m_NumFolds);
        if (!m_Inverse) {
  	instances = getInputFormat().testCV(m_NumFolds, m_Fold - 1);
        } else {
  	instances = getInputFormat().trainCV(m_NumFolds, m_Fold - 1);
        }
      }
      else {
        instances = getInputFormat();
      }
      
      flushInput();
  
      for (int i = 0; i < instances.numInstances(); i++) {
        push(instances.instance(i));
      }
  
      m_NewBatch = true;
      m_FirstBatchDone = true;
      return (numPendingOutput() != 0);
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5492 $");
    }
  
    /**
     * Main method for testing this class.
     *
     * @param argv should contain arguments to the filter: use -h for help
     */
    public static void main(String [] argv) {
      runFilter(new StratifiedRemoveFolds(), argv);
    }
  }
  