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.
   */
  
  /*
   *    AttributeSelectedClassifier.java
   *    Copyright (C) 2000 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.meta;
  
  import weka.attributeSelection.ASEvaluation;
  import weka.attributeSelection.ASSearch;
  import weka.attributeSelection.AttributeSelection;
  import weka.classifiers.SingleClassifierEnhancer;
  import weka.core.AdditionalMeasureProducer;
  import weka.core.Capabilities;
  import weka.core.Drawable;
  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.WeightedInstancesHandler;
  import weka.core.Capabilities.Capability;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -E &lt;attribute evaluator specification&gt;
   *  Full class name of attribute evaluator, followed
   *  by its options.
   *  eg: "weka.attributeSelection.CfsSubsetEval -L"
   *  (default weka.attributeSelection.CfsSubsetEval)</pre>
   * 
   * <pre> -S &lt;search method specification&gt;
   *  Full class name of search method, followed
   *  by its options.
   *  eg: "weka.attributeSelection.BestFirst -D 1"
   *  (default weka.attributeSelection.BestFirst)</pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   * <pre> -W
   *  Full name of base classifier.
   *  (default: weka.classifiers.trees.J48)</pre>
   * 
   * <pre> 
   * Options specific to classifier weka.classifiers.trees.J48:
   * </pre>
   * 
   * <pre> -U
   *  Use unpruned tree.</pre>
   * 
   * <pre> -C &lt;pruning confidence&gt;
   *  Set confidence threshold for pruning.
   *  (default 0.25)</pre>
   * 
   * <pre> -M &lt;minimum number of instances&gt;
   *  Set minimum number of instances per leaf.
   *  (default 2)</pre>
   * 
   * <pre> -R
   *  Use reduced error pruning.</pre>
   * 
   * <pre> -N &lt;number of folds&gt;
   *  Set number of folds for reduced error
   *  pruning. One fold is used as pruning set.
   *  (default 3)</pre>
   * 
   * <pre> -B
   *  Use binary splits only.</pre>
   * 
   * <pre> -S
   *  Don't perform subtree raising.</pre>
   * 
   * <pre> -L
   *  Do not clean up after the tree has been built.</pre>
   * 
   * <pre> -A
   *  Laplace smoothing for predicted probabilities.</pre>
   * 
   * <pre> -Q &lt;seed&gt;
   *  Seed for random data shuffling (default 1).</pre>
   * 
   <!-- options-end -->
   *
   * @author Mark Hall (mhall@cs.waikato.ac.nz)
   * @version $Revision: 1.26 $
   */
  public class AttributeSelectedClassifier 
    extends SingleClassifierEnhancer
    implements OptionHandler, Drawable, AdditionalMeasureProducer,
               WeightedInstancesHandler {
  
    /** for serialization */
    static final long serialVersionUID = -5951805453487947577L;
    
    /** The attribute selection object */
    protected AttributeSelection m_AttributeSelection = null;
  
    /** The attribute evaluator to use */
    protected ASEvaluation m_Evaluator = 
      new weka.attributeSelection.CfsSubsetEval();
  
    /** The search method to use */
    protected ASSearch m_Search = new weka.attributeSelection.BestFirst();
  
    /** The header of the dimensionally reduced data */
    protected Instances m_ReducedHeader;
  
    /** The number of class vals in the training data (1 if class is numeric) */
    protected int m_numClasses;
  
    /** The number of attributes selected by the attribute selection phase */
    protected double m_numAttributesSelected;
  
    /** The time taken to select attributes in milliseconds */
    protected double m_selectionTime;
  
    /** The time taken to select attributes AND build the classifier */
    protected double m_totalTime;
  
    
    /**
     * String describing default classifier.
     * 
     * @return the default classifier classname
     */
    protected String defaultClassifierString() {
      
      return "weka.classifiers.trees.J48";
    }
    
    /**
     * Default constructor.
     */
    public AttributeSelectedClassifier() {
      m_Classifier = new weka.classifiers.trees.J48();
    }
  
    /**
     * Returns a string describing this search method
     * @return a description of the search method suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return "Dimensionality of training and test data is reduced by "
        +"attribute selection before being passed on to a classifier.";
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
       Vector newVector = new Vector(3);
      
      newVector.addElement(new Option(
  	      "\tFull class name of attribute evaluator, followed\n"
  	      + "\tby its options.\n"
  	      + "\teg: \"weka.attributeSelection.CfsSubsetEval -L\"\n"
  	      + "\t(default weka.attributeSelection.CfsSubsetEval)",
  	      "E", 1, "-E <attribute evaluator specification>"));
  
      newVector.addElement(new Option(
  	      "\tFull class name of search method, followed\n"
  	      + "\tby its options.\n"
  	      + "\teg: \"weka.attributeSelection.BestFirst -D 1\"\n"
  	      + "\t(default weka.attributeSelection.BestFirst)",
  	      "S", 1, "-S <search method specification>"));
      
      Enumeration enu = super.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> -E &lt;attribute evaluator specification&gt;
     *  Full class name of attribute evaluator, followed
     *  by its options.
     *  eg: "weka.attributeSelection.CfsSubsetEval -L"
     *  (default weka.attributeSelection.CfsSubsetEval)</pre>
     * 
     * <pre> -S &lt;search method specification&gt;
     *  Full class name of search method, followed
     *  by its options.
     *  eg: "weka.attributeSelection.BestFirst -D 1"
     *  (default weka.attributeSelection.BestFirst)</pre>
     * 
     * <pre> -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console</pre>
     * 
     * <pre> -W
     *  Full name of base classifier.
     *  (default: weka.classifiers.trees.J48)</pre>
     * 
     * <pre> 
     * Options specific to classifier weka.classifiers.trees.J48:
     * </pre>
     * 
     * <pre> -U
     *  Use unpruned tree.</pre>
     * 
     * <pre> -C &lt;pruning confidence&gt;
     *  Set confidence threshold for pruning.
     *  (default 0.25)</pre>
     * 
     * <pre> -M &lt;minimum number of instances&gt;
     *  Set minimum number of instances per leaf.
     *  (default 2)</pre>
     * 
     * <pre> -R
     *  Use reduced error pruning.</pre>
     * 
     * <pre> -N &lt;number of folds&gt;
     *  Set number of folds for reduced error
     *  pruning. One fold is used as pruning set.
     *  (default 3)</pre>
     * 
     * <pre> -B
     *  Use binary splits only.</pre>
     * 
     * <pre> -S
     *  Don't perform subtree raising.</pre>
     * 
     * <pre> -L
     *  Do not clean up after the tree has been built.</pre>
     * 
     * <pre> -A
     *  Laplace smoothing for predicted probabilities.</pre>
     * 
     * <pre> -Q &lt;seed&gt;
     *  Seed for random data shuffling (default 1).</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 {
  
      // same for attribute evaluator
      String evaluatorString = Utils.getOption('E', options);
      if (evaluatorString.length() == 0)
        evaluatorString = weka.attributeSelection.CfsSubsetEval.class.getName();
      String [] evaluatorSpec = Utils.splitOptions(evaluatorString);
      if (evaluatorSpec.length == 0) {
        throw new Exception("Invalid attribute evaluator specification string");
      }
      String evaluatorName = evaluatorSpec[0];
      evaluatorSpec[0] = "";
      setEvaluator(ASEvaluation.forName(evaluatorName, evaluatorSpec));
  
      // same for search method
      String searchString = Utils.getOption('S', options);
      if (searchString.length() == 0)
        searchString = weka.attributeSelection.BestFirst.class.getName();
      String [] searchSpec = Utils.splitOptions(searchString);
      if (searchSpec.length == 0) {
        throw new Exception("Invalid search specification string");
      }
      String searchName = searchSpec[0];
      searchSpec[0] = "";
      setSearch(ASSearch.forName(searchName, searchSpec));
  
      super.setOptions(options);
    }
  
    /**
     * Gets the current settings of the Classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String [] getOptions() {
  
      String [] superOptions = super.getOptions();
      String [] options = new String [superOptions.length + 4];
  
      int current = 0;
  
      // same attribute evaluator
      options[current++] = "-E";
      options[current++] = "" +getEvaluatorSpec();
      
      // same for search
      options[current++] = "-S";
      options[current++] = "" + getSearchSpec();
  
      System.arraycopy(superOptions, 0, options, current, 
  		     superOptions.length);
      
      return options;
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String evaluatorTipText() {
      return "Set the attribute evaluator to use. This evaluator is used "
        +"during the attribute selection phase before the classifier is "
        +"invoked.";
    }
  
    /**
     * Sets the attribute evaluator
     *
     * @param evaluator the evaluator with all options set.
     */
    public void setEvaluator(ASEvaluation evaluator) {
      m_Evaluator = evaluator;
    }
  
    /**
     * Gets the attribute evaluator used
     *
     * @return the attribute evaluator
     */
    public ASEvaluation getEvaluator() {
      return m_Evaluator;
    }
  
    /**
     * Gets the evaluator specification string, which contains the class name of
     * the attribute evaluator and any options to it
     *
     * @return the evaluator string.
     */
    protected String getEvaluatorSpec() {
      
      ASEvaluation e = getEvaluator();
      if (e instanceof OptionHandler) {
        return e.getClass().getName() + " "
  	+ Utils.joinOptions(((OptionHandler)e).getOptions());
      }
      return e.getClass().getName();
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String searchTipText() {
      return "Set the search method. This search method is used "
        +"during the attribute selection phase before the classifier is "
        +"invoked.";
    }
    
    /**
     * Sets the search method
     *
     * @param search the search method with all options set.
     */
    public void setSearch(ASSearch search) {
      m_Search = search;
    }
  
    /**
     * Gets the search method used
     *
     * @return the search method
     */
    public ASSearch getSearch() {
      return m_Search;
    }
  
    /**
     * Gets the search specification string, which contains the class name of
     * the search method and any options to it
     *
     * @return the search string.
     */
    protected String getSearchSpec() {
      
      ASSearch s = getSearch();
      if (s instanceof OptionHandler) {
        return s.getClass().getName() + " "
  	+ Utils.joinOptions(((OptionHandler)s).getOptions());
      }
      return s.getClass().getName();
    }
  
    /**
     * Returns default capabilities of the classifier.
     *
     * @return      the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
      Capabilities	result;
      
      if (getEvaluator() == null)
        result = super.getCapabilities();
      else
        result = getEvaluator().getCapabilities();
      
      // set dependencies
      for (Capability cap: Capability.values())
        result.enableDependency(cap);
      
      return result;
    }
  
    /**
     * Build the classifier on the dimensionally reduced data.
     *
     * @param data the training data
     * @throws Exception if the classifier could not be built successfully
     */
    public void buildClassifier(Instances data) throws Exception {
      if (m_Classifier == null) {
        throw new Exception("No base classifier has been set!");
      }
  
      if (m_Evaluator == null) {
        throw new Exception("No attribute evaluator has been set!");
      }
  
      if (m_Search == null) {
        throw new Exception("No search method has been set!");
      }
     
      // can classifier handle the data?
      getCapabilities().testWithFail(data);
  
      // remove instances with missing class
      Instances newData = new Instances(data);
      newData.deleteWithMissingClass();
      
      if (newData.numInstances() == 0) {
        m_Classifier.buildClassifier(newData);
        return;
      }
      if (newData.classAttribute().isNominal()) {
        m_numClasses = newData.classAttribute().numValues();
      } else {
        m_numClasses = 1;
      }
  
      Instances resampledData = null;
      // check to see if training data has all equal weights
      double weight = newData.instance(0).weight();
      boolean ok = false;
      for (int i = 1; i < newData.numInstances(); i++) {
        if (newData.instance(i).weight() != weight) {
          ok = true;
          break;
        }
      }
      
      if (ok) {
        if (!(m_Evaluator instanceof WeightedInstancesHandler) || 
            !(m_Classifier instanceof WeightedInstancesHandler)) {
          Random r = new Random(1);
          for (int i = 0; i < 10; i++) {
            r.nextDouble();
          }
          resampledData = newData.resampleWithWeights(r);
        }
      } else {
        // all equal weights in the training data so just use as is
        resampledData = newData;
      }
  
      m_AttributeSelection = new AttributeSelection();
      m_AttributeSelection.setEvaluator(m_Evaluator);
      m_AttributeSelection.setSearch(m_Search);
      long start = System.currentTimeMillis();
      m_AttributeSelection.
        SelectAttributes((m_Evaluator instanceof WeightedInstancesHandler) 
                         ? newData
                         : resampledData);
      long end = System.currentTimeMillis();
      if (m_Classifier instanceof WeightedInstancesHandler) {
        newData = m_AttributeSelection.reduceDimensionality(newData);
        m_Classifier.buildClassifier(newData);
      } else {
        resampledData = m_AttributeSelection.reduceDimensionality(resampledData);
        m_Classifier.buildClassifier(resampledData);
      }
  
      long end2 = System.currentTimeMillis();
      m_numAttributesSelected = m_AttributeSelection.numberAttributesSelected();
      m_ReducedHeader = 
        new Instances((m_Classifier instanceof WeightedInstancesHandler) ?
                      newData
                      : resampledData, 0);
      m_selectionTime = (double)(end - start);
      m_totalTime = (double)(end2 - start);
    }
  
    /**
     * Classifies a given instance after attribute selection
     *
     * @param instance the instance to be classified
     * @return the class distribution
     * @throws Exception if instance could not be classified
     * successfully
     */
    public double [] distributionForInstance(Instance instance)
      throws Exception {
  
      Instance newInstance;
      if (m_AttributeSelection == null) {
        //      throw new Exception("AttributeSelectedClassifier: No model built yet!");
        newInstance = instance;
      } else {
        newInstance = m_AttributeSelection.reduceDimensionality(instance);
      }
  
      return m_Classifier.distributionForInstance(newInstance);
    }
  
    /**
     *  Returns the type of graph this classifier
     *  represents.
     *  
     *  @return the type of graph
     */   
    public int graphType() {
      
      if (m_Classifier instanceof Drawable)
        return ((Drawable)m_Classifier).graphType();
      else 
        return Drawable.NOT_DRAWABLE;
    }
  
    /**
     * Returns graph describing the classifier (if possible).
     *
     * @return the graph of the classifier in dotty format
     * @throws Exception if the classifier cannot be graphed
     */
    public String graph() throws Exception {
      
      if (m_Classifier instanceof Drawable)
        return ((Drawable)m_Classifier).graph();
      else throw new Exception("Classifier: " + getClassifierSpec()
  			     + " cannot be graphed");
    }
  
    /**
     * Output a representation of this classifier
     * 
     * @return a representation of this classifier
     */
    public String toString() {
      if (m_AttributeSelection == null) {
        return "AttributeSelectedClassifier: No attribute selection possible.\n\n"
  	+m_Classifier.toString();
      }
  
      StringBuffer result = new StringBuffer();
      result.append("AttributeSelectedClassifier:\n\n");
      result.append(m_AttributeSelection.toResultsString());
      result.append("\n\nHeader of reduced data:\n"+m_ReducedHeader.toString());
      result.append("\n\nClassifier Model\n"+m_Classifier.toString());
  
      return result.toString();
    }
  
    /**
     * Additional measure --- number of attributes selected
     * @return the number of attributes selected
     */
    public double measureNumAttributesSelected() {
      return m_numAttributesSelected;
    }
  
    /**
     * Additional measure --- time taken (milliseconds) to select the attributes
     * @return the time taken to select attributes
     */
    public double measureSelectionTime() {
      return m_selectionTime;
    }
  
    /**
     * Additional measure --- time taken (milliseconds) to select attributes
     * and build the classifier
     * @return the total time (select attributes + build classifier)
     */
    public double measureTime() {
      return m_totalTime;
    }
  
    /**
     * Returns an enumeration of the additional measure names
     * @return an enumeration of the measure names
     */
    public Enumeration enumerateMeasures() {
      Vector newVector = new Vector(3);
      newVector.addElement("measureNumAttributesSelected");
      newVector.addElement("measureSelectionTime");
      newVector.addElement("measureTime");
      if (m_Classifier instanceof AdditionalMeasureProducer) {
        Enumeration en = ((AdditionalMeasureProducer)m_Classifier).
  	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 (additionalMeasureName.compareToIgnoreCase("measureNumAttributesSelected") == 0) {
        return measureNumAttributesSelected();
      } else if (additionalMeasureName.compareToIgnoreCase("measureSelectionTime") == 0) {
        return measureSelectionTime();
      } else if (additionalMeasureName.compareToIgnoreCase("measureTime") == 0) {
        return measureTime();
      } else if (m_Classifier instanceof AdditionalMeasureProducer) {
        return ((AdditionalMeasureProducer)m_Classifier).
  	getMeasure(additionalMeasureName);
      } else {
        throw new IllegalArgumentException(additionalMeasureName 
  			  + " not supported (AttributeSelectedClassifier)");
      }
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.26 $");
    }
  
    /**
     * Main method for testing this class.
     *
     * @param argv should contain the following arguments:
     * -t training file [-T test file] [-c class index]
     */
    public static void main(String [] argv) {
      runClassifier(new AttributeSelectedClassifier(), argv);
    }
  }
  