decisiontable.java

来自「Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等」· Java 代码 · 共 1,531 行 · 第 1/3 页

  /**
   * Gets the number of folds for cross validation
   *
   * @return the number of cross validation folds
   */
  public int getCrossVal() {

    return m_CVFolds;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String maxStaleTipText() {
    return "Sets the number of non improving decision tables to consider " 
      + "before abandoning the search.";
  }

  /**
   * Sets the number of non improving decision tables to consider
   * before abandoning the search.
   *
   * @param stale the number of nodes
   */
  public void setMaxStale(int stale) {

    m_maxStale = stale;
  }

  /**
   * Gets the number of non improving decision tables
   *
   * @return the number of non improving decision tables
   */
  public int getMaxStale() {

    return m_maxStale;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useIBkTipText() {
    return "Sets whether IBk should be used instead of the majority class.";
  }

  /**
   * Sets whether IBk should be used instead of the majority class
   *
   * @param ibk true if IBk is to be used
   */
  public void setUseIBk(boolean ibk) {

    m_useIBk = ibk;
  }
  
  /**
   * Gets whether IBk is being used instead of the majority class
   *
   * @return true if IBk is being used
   */
  public boolean getUseIBk() {

    return m_useIBk;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String displayRulesTipText() {
    return "Sets whether rules are to be printed.";
  }

  /**
   * Sets whether rules are to be printed
   *
   * @param rules true if rules are to be printed
   */
  public void setDisplayRules(boolean rules) {

    m_displayRules = rules;
  }
  
  /**
   * Gets whether rules are being printed
   *
   * @return true if rules are being printed
   */
  public boolean getDisplayRules() {

    return m_displayRules;
  }

  /**
   * Parses the options for this object. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -S &lt;number of non improving nodes&gt;
   *  Number of fully expanded non improving subsets to consider
   *  before terminating a best first search.
   *  Use in conjunction with -B. (Default = 5)</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> -I
   *  Use nearest neighbour instead of global table majority.</pre>
   * 
   * <pre> -R
   *  Display decision table rules.
   * </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);
    }

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

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

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

  /**
   * Gets the current settings of the classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {

    String [] options = new String [7];
    int current = 0;

    options[current++] = "-X"; options[current++] = "" + m_CVFolds;
    options[current++] = "-S"; options[current++] = "" + m_maxStale;
    if (m_useIBk) {
      options[current++] = "-I";
    }
    if (m_displayRules) {
      options[current++] = "-R";
    }
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // 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.NUMERIC_CLASS);
    result.enable(Capability.DATE_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);
    
    return result;
  }
  
  /**
   * 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().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());
    
    best_first();
    
    // 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_theInstances = Filter.useFilter(m_theInstances, m_delTransform);
    
    // reset the number of attributes
    m_numAttributes = m_theInstances.numAttributes();
    
    // create hash table
    m_entries = new Hashtable((int)(m_theInstances.numInstances() * 1.5));
    
    // insert instances into the hash table
    for (int i = 0; i < m_numInstances; i++) {
      Instance inst = m_theInstances.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
    m_theInstances = new Instances(m_theInstances, 0);
  }

  /**
   * Calculates the class membership probabilities for the given 
   * test instance.
   *
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @throws Exception if distribution can't be computed
   */
  public double [] distributionForInstance(Instance instance)
       throws Exception {

    hashKey thekey;
    double [] tempDist;
    double [] normDist;

    m_disTransform.input(instance);
    m_disTransform.batchFinished();
    instance = m_disTransform.output();

    m_delTransform.input(instance);
    m_delTransform.batchFinished();
    instance = m_delTransform.output();

    thekey = new hashKey(instance, instance.numAttributes(), false);
    
    // if this one is not in the table
    if ((tempDist = (double [])m_entries.get(thekey)) == null) {
      if (m_useIBk) {
	tempDist = m_ibk.distributionForInstance(instance);
      } else {
	if (!m_classIsNominal) {
	  tempDist = new double[1];
	  tempDist[0] = m_majority;
	} else {
	  tempDist = new double [m_theInstances.classAttribute().numValues()];
	  tempDist[(int)m_majority] = 1.0;
	}
      }
    } else {
      if (!m_classIsNominal) {
	normDist = new double[1];
	normDist[0] = (tempDist[0] / tempDist[1]);
	tempDist = normDist;
      } else {
	
	// normalise distribution
	normDist = new double [tempDist.length];
	System.arraycopy(tempDist,0,normDist,0,tempDist.length);
	Utils.normalize(normDist);
	tempDist = normDist;
      }
    }
    return tempDist;
  }

  /**
   * Returns a string description of the features selected
   *
   * @return a string of features
   */
  public String printFeatures() {

    int i;
    String s = "";
   
    for (i=0;i<m_decisionFeatures.length;i++) {
      if (i==0) {
	s = ""+(m_decisionFeatures[i]+1);
      } else {
	s += ","+(m_decisionFeatures[i]+1);
      }
    }
    return s;
  }

  /**
   * Returns the number of rules
   * @return the number of rules
   */
  public double measureNumRules() {
    return m_entries.size();
  }

  /**
   * Returns an enumeration of the additional measure names
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector(1);
    newVector.addElement("measureNumRules");
    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("measureNumRules") == 0) {
      return measureNumRules();
    } else {
      throw new IllegalArgumentException(additionalMeasureName 
			  + " not supported (DecisionTable)");
    }
  }

  /**
   * 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("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_theInstances.numAttributes();i++) {
	  if (m_theInstances.attribute(i).name().length() > maxColWidth) {
	    maxColWidth = m_theInstances.attribute(i).name().length();
	  }

	  if (m_classIsNominal || (i != m_theInstances.classIndex())) {
	    Enumeration e = m_theInstances.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_theInstances.numAttributes();i++) {
	  if (m_theInstances.classIndex() != i) {
	    int d = maxColWidth - m_theInstances.attribute(i).name().length();
	    tm.append(m_theInstances.attribute(i).name());
	    for (int j=0;j<d+1;j++) {
	      tm.append(" ");
	    }
	  }
	}
	tm.append(m_theInstances.attribute(m_theInstances.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()) {
	  hashKey tt = (hashKey)e.nextElement();
	  text.append(tt.toString(m_theInstances,maxColWidth));
	  double [] ClassDist = (double []) m_entries.get(tt);

	  if (m_classIsNominal) {
	    int m = Utils.maxIndex(ClassDist);
	    try {
	      text.append(m_theInstances.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();
    }
  }

  /**
   * Main method for testing this class.
   *
   * @param argv the command-line options
   */
  public static void main(String [] argv) {
    
    Classifier scheme;
    
    try {
      scheme = new DecisionTable();
      System.out.println(Evaluation.evaluateModel(scheme,argv));
    }
    catch (Exception e) {
      e.printStackTrace();
      System.out.println(e.getMessage());
    }
  }
}