Beispiel #1
0
  /**
   * 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();
  }
Beispiel #2
0
  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(7);

    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>"));

    newVector.addElement(
        new Option(
            "\tUse cross validation to evaluate features.\n"
                + "\tUse number of folds = 1 for leave one out CV.\n"
                + "\t(Default = leave one out CV)",
            "X",
            1,
            "-X <number of folds>"));

    newVector.addElement(
        new Option(
            "\tPerformance evaluation measure to use for selecting attributes.\n"
                + "\t(Default = accuracy for discrete class and rmse for numeric class)",
            "E",
            1,
            "-E <acc | rmse | mae | auc>"));

    newVector.addElement(
        new Option("\tUse nearest neighbour instead of global table majority.", "I", 0, "-I"));

    newVector.addElement(new Option("\tDisplay decision table rules.\n", "R", 0, "-R"));

    newVector.addElement(
        new Option(
            "",
            "",
            0,
            "\nOptions specific to search method " + m_search.getClass().getName() + ":"));
    Enumeration enu = ((OptionHandler) m_search).listOptions();
    while (enu.hasMoreElements()) {
      newVector.addElement(enu.nextElement());
    }
    return newVector.elements();
  }
Beispiel #3
0
  /**
   * Parses the options for this object.
   *
   * <p>
   * <!-- options-start -->
   * Valid options are:
   *
   * <p>
   *
   * <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> -X &lt;number of folds&gt;
   *  Use cross validation to evaluate features.
   *  Use number of folds = 1 for leave one out CV.
   *  (Default = leave one out CV)</pre>
   *
   * <pre> -E &lt;acc | rmse | mae | auc&gt;
   *  Performance evaluation measure to use for selecting attributes.
   *  (Default = accuracy for discrete class and rmse for numeric class)</pre>
   *
   * <pre> -I
   *  Use nearest neighbour instead of global table majority.</pre>
   *
   * <pre> -R
   *  Display decision table rules.
   * </pre>
   *
   * <pre>
   * Options specific to search method weka.attributeSelection.BestFirst:
   * </pre>
   *
   * <pre> -P &lt;start set&gt;
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.</pre>
   *
   * <pre> -D &lt;0 = backward | 1 = forward | 2 = bi-directional&gt;
   *  Direction of search. (default = 1).</pre>
   *
   * <pre> -N &lt;num&gt;
   *  Number of non-improving nodes to
   *  consider before terminating search.</pre>
   *
   * <pre> -S &lt;num&gt;
   *  Size of lookup cache for evaluated subsets.
   *  Expressed as a multiple of the number of
   *  attributes in the data set. (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 {

    String optionString;

    resetOptions();

    optionString = Utils.getOption('X', options);
    if (optionString.length() != 0) {
      m_CVFolds = Integer.parseInt(optionString);
    }

    m_useIBk = Utils.getFlag('I', options);

    m_displayRules = Utils.getFlag('R', options);

    optionString = Utils.getOption('E', options);
    if (optionString.length() != 0) {
      if (optionString.equals("acc")) {
        setEvaluationMeasure(new SelectedTag(EVAL_ACCURACY, TAGS_EVALUATION));
      } else if (optionString.equals("rmse")) {
        setEvaluationMeasure(new SelectedTag(EVAL_RMSE, TAGS_EVALUATION));
      } else if (optionString.equals("mae")) {
        setEvaluationMeasure(new SelectedTag(EVAL_MAE, TAGS_EVALUATION));
      } else if (optionString.equals("auc")) {
        setEvaluationMeasure(new SelectedTag(EVAL_AUC, TAGS_EVALUATION));
      } else {
        throw new IllegalArgumentException("Invalid evaluation measure");
      }
    }

    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 IllegalArgumentException("Invalid search specification string");
    }
    String searchName = searchSpec[0];
    searchSpec[0] = "";
    setSearch(ASSearch.forName(searchName, searchSpec));
  }
Beispiel #4
0
  /**
   * Returns a description of the classifier.
   *
   * @return a description of the classifier as a string.
   */
  public String toString() {

    if (m_entries == null) {
      return "Decision Table: No model built yet.";
    } else {
      StringBuffer text = new StringBuffer();

      text.append(
          "Decision Table:"
              + "\n\nNumber of training instances: "
              + m_numInstances
              + "\nNumber of Rules : "
              + m_entries.size()
              + "\n");

      if (m_useIBk) {
        text.append("Non matches covered by IB1.\n");
      } else {
        text.append("Non matches covered by Majority class.\n");
      }

      text.append(m_search.toString());
      /*text.append("Best first search for feature set,\nterminated after "+
      m_maxStale+" non improving subsets.\n"); */

      text.append("Evaluation (for feature selection): CV ");
      if (m_CVFolds > 1) {
        text.append("(" + m_CVFolds + " fold) ");
      } else {
        text.append("(leave one out) ");
      }
      text.append("\nFeature set: " + printFeatures());

      if (m_displayRules) {

        // find out the max column width
        int maxColWidth = 0;
        for (int i = 0; i < m_dtInstances.numAttributes(); i++) {
          if (m_dtInstances.attribute(i).name().length() > maxColWidth) {
            maxColWidth = m_dtInstances.attribute(i).name().length();
          }

          if (m_classIsNominal || (i != m_dtInstances.classIndex())) {
            Enumeration e = m_dtInstances.attribute(i).enumerateValues();
            while (e.hasMoreElements()) {
              String ss = (String) e.nextElement();
              if (ss.length() > maxColWidth) {
                maxColWidth = ss.length();
              }
            }
          }
        }

        text.append("\n\nRules:\n");
        StringBuffer tm = new StringBuffer();
        for (int i = 0; i < m_dtInstances.numAttributes(); i++) {
          if (m_dtInstances.classIndex() != i) {
            int d = maxColWidth - m_dtInstances.attribute(i).name().length();
            tm.append(m_dtInstances.attribute(i).name());
            for (int j = 0; j < d + 1; j++) {
              tm.append(" ");
            }
          }
        }
        tm.append(m_dtInstances.attribute(m_dtInstances.classIndex()).name() + "  ");

        for (int i = 0; i < tm.length() + 10; i++) {
          text.append("=");
        }
        text.append("\n");
        text.append(tm);
        text.append("\n");
        for (int i = 0; i < tm.length() + 10; i++) {
          text.append("=");
        }
        text.append("\n");

        Enumeration e = m_entries.keys();
        while (e.hasMoreElements()) {
          DecisionTableHashKey tt = (DecisionTableHashKey) e.nextElement();
          text.append(tt.toString(m_dtInstances, maxColWidth));
          double[] ClassDist = (double[]) m_entries.get(tt);

          if (m_classIsNominal) {
            int m = Utils.maxIndex(ClassDist);
            try {
              text.append(m_dtInstances.classAttribute().value(m) + "\n");
            } catch (Exception ee) {
              System.out.println(ee.getMessage());
            }
          } else {
            text.append((ClassDist[0] / ClassDist[1]) + "\n");
          }
        }

        for (int i = 0; i < tm.length() + 10; i++) {
          text.append("=");
        }
        text.append("\n");
        text.append("\n");
      }
      return text.toString();
    }
  }
Beispiel #5
0
  /**
   * Generates the classifier.
   *
   * @param data set of instances serving as training data
   * @throws Exception if the classifier has not been generated successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    m_theInstances = new Instances(data);
    m_theInstances.deleteWithMissingClass();

    m_rr = new Random(1);

    if (m_theInstances.classAttribute().isNominal()) { // 	 Set up class priors
      m_classPriorCounts = new double[data.classAttribute().numValues()];
      Arrays.fill(m_classPriorCounts, 1.0);
      for (int i = 0; i < data.numInstances(); i++) {
        Instance curr = data.instance(i);
        m_classPriorCounts[(int) curr.classValue()] += curr.weight();
      }
      m_classPriors = m_classPriorCounts.clone();
      Utils.normalize(m_classPriors);
    }

    setUpEvaluator();

    if (m_theInstances.classAttribute().isNumeric()) {
      m_disTransform = new weka.filters.unsupervised.attribute.Discretize();
      m_classIsNominal = false;

      // use binned discretisation if the class is numeric
      ((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setBins(10);
      ((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setInvertSelection(true);

      // Discretize all attributes EXCEPT the class
      String rangeList = "";
      rangeList += (m_theInstances.classIndex() + 1);
      // System.out.println("The class col: "+m_theInstances.classIndex());

      ((weka.filters.unsupervised.attribute.Discretize) m_disTransform)
          .setAttributeIndices(rangeList);
    } else {
      m_disTransform = new weka.filters.supervised.attribute.Discretize();
      ((weka.filters.supervised.attribute.Discretize) m_disTransform).setUseBetterEncoding(true);
      m_classIsNominal = true;
    }

    m_disTransform.setInputFormat(m_theInstances);
    m_theInstances = Filter.useFilter(m_theInstances, m_disTransform);

    m_numAttributes = m_theInstances.numAttributes();
    m_numInstances = m_theInstances.numInstances();
    m_majority = m_theInstances.meanOrMode(m_theInstances.classAttribute());

    // Perform the search
    int[] selected = m_search.search(m_evaluator, m_theInstances);

    m_decisionFeatures = new int[selected.length + 1];
    System.arraycopy(selected, 0, m_decisionFeatures, 0, selected.length);
    m_decisionFeatures[m_decisionFeatures.length - 1] = m_theInstances.classIndex();

    // reduce instances to selected features
    m_delTransform = new Remove();
    m_delTransform.setInvertSelection(true);

    // set features to keep
    m_delTransform.setAttributeIndicesArray(m_decisionFeatures);
    m_delTransform.setInputFormat(m_theInstances);
    m_dtInstances = Filter.useFilter(m_theInstances, m_delTransform);

    // reset the number of attributes
    m_numAttributes = m_dtInstances.numAttributes();

    // create hash table
    m_entries = new Hashtable((int) (m_dtInstances.numInstances() * 1.5));

    // insert instances into the hash table
    for (int i = 0; i < m_numInstances; i++) {
      Instance inst = m_dtInstances.instance(i);
      insertIntoTable(inst, null);
    }

    // Replace the global table majority with nearest neighbour?
    if (m_useIBk) {
      m_ibk = new IBk();
      m_ibk.buildClassifier(m_theInstances);
    }

    // Save memory
    if (m_saveMemory) {
      m_theInstances = new Instances(m_theInstances, 0);
      m_dtInstances = new Instances(m_dtInstances, 0);
    }
    m_evaluation = null;
  }