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.
   */
  
  /*
   *    OneRAttributeEval.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.attributeSelection;
  
  import weka.classifiers.Classifier;
  import weka.classifiers.AbstractClassifier;
  import weka.classifiers.Evaluation;
  import weka.core.Capabilities;
  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.unsupervised.attribute.Remove;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /** 
   <!-- globalinfo-start -->
   * OneRAttributeEval :<br/>
   * <br/>
   * Evaluates the worth of an attribute by using the OneR classifier.<br/>
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -S &lt;seed&gt;
   *  Random number seed for cross validation
   *  (default = 1)</pre>
   * 
   * <pre> -F &lt;folds&gt;
   *  Number of folds for cross validation
   *  (default = 10)</pre>
   * 
   * <pre> -D
   *  Use training data for evaluation rather than cross validaton</pre>
   * 
   * <pre> -B &lt;minimum bucket size&gt;
   *  Minimum number of objects in a bucket
   *  (passed on to OneR, default = 6)</pre>
   * 
   <!-- options-end -->
   *
   * @author Mark Hall (mhall@cs.waikato.ac.nz)
   * @version $Revision: 5928 $
   */
  public class OneRAttributeEval
    extends ASEvaluation
    implements AttributeEvaluator, OptionHandler {
    
    /** for serialization */
    static final long serialVersionUID = 4386514823886856980L;
  
    /** The training instances */
    private Instances m_trainInstances;
  
    /** The class index */
    private int m_classIndex;
  
    /** The number of attributes */
    private int m_numAttribs;
  
    /** The number of instances */
    private int m_numInstances;
  
    /** Random number seed */
    private int m_randomSeed;
  
    /** Number of folds for cross validation */
    private int m_folds;
  
    /** Use training data to evaluate merit rather than x-val */
    private boolean m_evalUsingTrainingData;
  
    /** Passed on to OneR */
    private int m_minBucketSize;
  
  
    /**
     * Returns a string describing this attribute evaluator
     * @return a description of the evaluator suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return "OneRAttributeEval :\n\nEvaluates the worth of an attribute by "
        +"using the OneR classifier.\n";
    }
  
    /**
     * Returns a string for this option suitable for display in the gui
     * as a tip text
     *
     * @return a string describing this option
     */
    public String seedTipText() {
      return "Set the seed for use in cross validation.";
    }
  
    /**
     * Set the random number seed for cross validation
     *
     * @param seed the seed to use
     */
    public void setSeed(int seed) {
      m_randomSeed = seed;
    }
  
    /**
     * Get the random number seed
     *
     * @return an <code>int</code> value
     */
    public int getSeed() {
      return m_randomSeed;
    }
  
    /**
     * Returns a string for this option suitable for display in the gui
     * as a tip text
     *
     * @return a string describing this option
     */
    public String foldsTipText() {
      return "Set the number of folds for cross validation.";
    }
  
    /**
     * Set the number of folds to use for cross validation
     *
     * @param folds the number of folds
     */
    public void setFolds(int folds) {
      m_folds = folds;
      if (m_folds < 2) {
        m_folds = 2;
      }
    }
     
    /**
     * Get the number of folds used for cross validation
     *
     * @return the number of folds
     */
    public int getFolds() {
      return m_folds;
    }
  
    /**
     * Returns a string for this option suitable for display in the gui
     * as a tip text
     *
     * @return a string describing this option
     */
    public String evalUsingTrainingDataTipText() {
      return "Use the training data to evaluate attributes rather than "
        + "cross validation.";
    }
  
    /**
     * Use the training data to evaluate attributes rather than cross validation
     *
     * @param e true if training data is to be used for evaluation
     */
    public void setEvalUsingTrainingData(boolean e) {
      m_evalUsingTrainingData = e;
    }
  
    /**
     * Returns a string for this option suitable for display in the gui
     * as a tip text
     *
     * @return a string describing this option
     */
    public String minimumBucketSizeTipText() {
      return "The minimum number of objects in a bucket "
        + "(passed to OneR).";
    }
  
    /**
     * Set the minumum bucket size used by OneR
     *
     * @param minB the minimum bucket size to use
     */
    public void setMinimumBucketSize(int minB) {
      m_minBucketSize = minB;
    }
  
    /**
     * Get the minimum bucket size used by oneR
     *
     * @return the minimum bucket size used
     */
    public int getMinimumBucketSize() {
      return m_minBucketSize;
    }
  
    /**
     * Returns true if the training data is to be used for evaluation
     *
     * @return true if training data is to be used for evaluation
     */
    public boolean getEvalUsingTrainingData() {
      return m_evalUsingTrainingData;
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
  
      Vector newVector = new Vector(4);
  
      newVector.addElement(new Option(
          "\tRandom number seed for cross validation\n"
          + "\t(default = 1)",
          "S", 1, "-S <seed>"));
  
      newVector.addElement(new Option(
          "\tNumber of folds for cross validation\n"
          + "\t(default = 10)",
          "F", 1, "-F <folds>"));
  
      newVector.addElement(new Option(
          "\tUse training data for evaluation rather than cross validaton",
          "D", 0, "-D"));
  
      newVector.addElement(new Option(
          "\tMinimum number of objects in a bucket\n"
          + "\t(passed on to "
          +"OneR, default = 6)",
          "B", 1, "-B <minimum bucket size>"));
  
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -S &lt;seed&gt;
     *  Random number seed for cross validation
     *  (default = 1)</pre>
     * 
     * <pre> -F &lt;folds&gt;
     *  Number of folds for cross validation
     *  (default = 10)</pre>
     * 
     * <pre> -D
     *  Use training data for evaluation rather than cross validaton</pre>
     * 
     * <pre> -B &lt;minimum bucket size&gt;
     *  Minimum number of objects in a bucket
     *  (passed on to OneR, default = 6)</pre>
     * 
     <!-- options-end -->
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions(String [] options) throws Exception {
      String temp = Utils.getOption('S', options);
  
      if (temp.length() != 0) {
        setSeed(Integer.parseInt(temp));
      }
      
      temp = Utils.getOption('F', options);
      if (temp.length() != 0) {
        setFolds(Integer.parseInt(temp));
      }
  
      temp = Utils.getOption('B', options);
      if (temp.length() != 0) {
        setMinimumBucketSize(Integer.parseInt(temp));
      }
      
      setEvalUsingTrainingData(Utils.getFlag('D', options));
      Utils.checkForRemainingOptions(options);
    }
  
    /**
     * returns the current setup.
     * 
     * @return the options of the current setup
     */
    public String[] getOptions() {
      String [] options = new String [7];
      int current = 0;
      
      if (getEvalUsingTrainingData()) {
        options[current++] = "-D";
      }
      
      options[current++] = "-S";
      options[current++] = "" + getSeed();
      options[current++] = "-F";
      options[current++] = "" + getFolds();
      options[current++] = "-B";
      options[current++] = "" + getMinimumBucketSize();
  
      while (current < options.length) {
        options[current++] = "";
      }
      return options;
    }
  
    /**
     * Constructor
     */
    public OneRAttributeEval () {
      resetOptions();
    }
  
    /**
     * Returns the capabilities of this evaluator.
     *
     * @return            the capabilities of this evaluator
     * @see               Capabilities
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
      
      // attributes
      result.enable(Capability.NOMINAL_ATTRIBUTES);
      result.enable(Capability.NUMERIC_ATTRIBUTES);
      result.enable(Capability.DATE_ATTRIBUTES);
      result.enable(Capability.MISSING_VALUES);
      
      // class
      result.enable(Capability.NOMINAL_CLASS);
      result.enable(Capability.MISSING_CLASS_VALUES);
      
      return result;
    }
  
    /**
     * Initializes a OneRAttribute attribute evaluator.
     * Discretizes all attributes that are numeric.
     *
     * @param data set of instances serving as training data 
     * @throws Exception if the evaluator has not been 
     * generated successfully
     */
    public void buildEvaluator (Instances data)
      throws Exception {
      
      // can evaluator handle data?
      getCapabilities().testWithFail(data);
  
      m_trainInstances = data;
      m_classIndex = m_trainInstances.classIndex();
      m_numAttribs = m_trainInstances.numAttributes();
      m_numInstances = m_trainInstances.numInstances();
    }
  
  
    /**
     * rests to defaults.
     */
    protected void resetOptions () {
      m_trainInstances = null;
      m_randomSeed = 1;
      m_folds = 10;
      m_evalUsingTrainingData = false;
      m_minBucketSize = 6; // default used by OneR
    }
  
  
    /**
     * evaluates an individual attribute by measuring the amount
     * of information gained about the class given the attribute.
     *
     * @param attribute the index of the attribute to be evaluated
     * @throws Exception if the attribute could not be evaluated
     */
    public double evaluateAttribute (int attribute)
      throws Exception {
      int[] featArray = new int[2]; // feat + class
      double errorRate;
      Evaluation o_Evaluation;
      Remove delTransform = new Remove();
      delTransform.setInvertSelection(true);
      // copy the instances
      Instances trainCopy = new Instances(m_trainInstances);
      featArray[0] = attribute;
      featArray[1] = trainCopy.classIndex();
      delTransform.setAttributeIndicesArray(featArray);
      delTransform.setInputFormat(trainCopy);
      trainCopy = Filter.useFilter(trainCopy, delTransform);
      o_Evaluation = new Evaluation(trainCopy);
      String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
      Classifier oneR = AbstractClassifier.forName("weka.classifiers.rules.OneR", oneROpts);
      if (m_evalUsingTrainingData) {
        oneR.buildClassifier(trainCopy);
        o_Evaluation.evaluateModel(oneR, trainCopy);
      } else {
        /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
                trainCopy, 10, 
                null, new Random(m_randomSeed)); */
        o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed));
      }
      errorRate = o_Evaluation.errorRate();
      return  (1 - errorRate)*100.0;
    }
  
  
    /**
     * Return a description of the evaluator
     * @return description as a string
     */
    public String toString () {
      StringBuffer text = new StringBuffer();
  
      if (m_trainInstances == null) {
        text.append("\tOneR feature evaluator has not been built yet");
      }
      else {
        text.append("\tOneR feature evaluator.\n\n");
        text.append("\tUsing ");
        if (m_evalUsingTrainingData) {
          text.append("training data for evaluation of attributes.");
        } else {
          text.append(""+getFolds()+" fold cross validation for evaluating "
                      +"attributes.");
        }
        text.append("\n\tMinimum bucket size for OneR: "
                    +getMinimumBucketSize());
      }
  
      text.append("\n");
      return  text.toString();
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5928 $");
    }
  
    // ============
    // Test method.
    // ============
    /**
     * Main method for testing this class.
     *
     * @param args the options
     */
    public static void main (String[] args) {
      runEvaluator(new OneRAttributeEval(), args);
    }
  }
  