costsensitiveclassifier.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

  public String globalInfo() {

    return "A metaclassifier that makes its base classifier cost-sensitive. "
      + "Two methods can be used to introduce cost-sensitivity: reweighting "
      + "training instances according to the total cost assigned to each "
      + "class; or predicting the class with minimum expected "
      + "misclassification cost (rather than the most likely class). "
      + "Performance can often be "
      + "improved by using a Bagged classifier to improve the probability "
      + "estimates of the base classifier.";
  }

  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String costMatrixSourceTipText() {

    return "Sets where to get the cost matrix. The two options are"
      + "to use the supplied explicit cost matrix (the setting of the "
      + "costMatrix property), or to load a cost matrix from a file when "
      + "required (this file will be loaded from the directory set by the "
      + "onDemandDirectory property and will be named relation_name" 
      + CostMatrix.FILE_EXTENSION + ").";
  }

  /**
   * Gets the source location method of the cost matrix. Will be one of
   * MATRIX_ON_DEMAND or MATRIX_SUPPLIED.
   *
   * @return the cost matrix source.
   */
  public SelectedTag getCostMatrixSource() {

    return new SelectedTag(m_MatrixSource, TAGS_MATRIX_SOURCE);
  }
  
  /**
   * Sets the source location of the cost matrix. Values other than
   * MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.
   *
   * @param newMethod the cost matrix location method.
   */
  public void setCostMatrixSource(SelectedTag newMethod) {
    
    if (newMethod.getTags() == TAGS_MATRIX_SOURCE) {
      m_MatrixSource = newMethod.getSelectedTag().getID();
    }
  }

  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String onDemandDirectoryTipText() {

    return "Sets the directory where cost files are loaded from. This option "
      + "is used when the costMatrixSource is set to \"On Demand\".";
  }

  /**
   * Returns the directory that will be searched for cost files when
   * loading on demand.
   *
   * @return The cost file search directory.
   */
  public File getOnDemandDirectory() {

    return m_OnDemandDirectory;
  }

  /**
   * Sets the directory that will be searched for cost files when
   * loading on demand.
   *
   * @param newDir The cost file search directory.
   */
  public void setOnDemandDirectory(File newDir) {

    if (newDir.isDirectory()) {
      m_OnDemandDirectory = newDir;
    } else {
      m_OnDemandDirectory = new File(newDir.getParent());
    }
    m_MatrixSource = MATRIX_ON_DEMAND;
  }

  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String minimizeExpectedCostTipText() {

    return "Sets whether the minimum expected cost criteria will be used. If "
      + "this is false, the training data will be reweighted according to the "
      + "costs assigned to each class. If true, the minimum expected cost "
      + "criteria will be used.";
  }

  /**
   * Gets the value of MinimizeExpectedCost.
   *
   * @return Value of MinimizeExpectedCost.
   */
  public boolean getMinimizeExpectedCost() {
    
    return m_MinimizeExpectedCost;
  }
  
  /**
   * Set the value of MinimizeExpectedCost.
   *
   * @param newMinimizeExpectedCost Value to assign to MinimizeExpectedCost.
   */
  public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost) {
    
    m_MinimizeExpectedCost = newMinimizeExpectedCost;
  }
  
  /**
   * Gets the classifier specification string, which contains the class name of
   * the classifier and any options to the classifier
   *
   * @return the classifier string.
   */
  protected String getClassifierSpec() {
    
    Classifier c = getClassifier();
    if (c instanceof OptionHandler) {
      return c.getClass().getName() + " "
	+ Utils.joinOptions(((OptionHandler)c).getOptions());
    }
    return c.getClass().getName();
  }
  
  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String costMatrixTipText() {
    return "Sets the cost matrix explicitly. This matrix is used if the "
      + "costMatrixSource property is set to \"Supplied\".";
  }

  /**
   * Gets the misclassification cost matrix.
   *
   * @return the cost matrix
   */
  public CostMatrix getCostMatrix() {
    
    return m_CostMatrix;
  }
  
  /**
   * Sets the misclassification cost matrix.
   *
   * @param newCostMatrix the cost matrix
   */
  public void setCostMatrix(CostMatrix newCostMatrix) {
    
    m_CostMatrix = newCostMatrix;
    m_MatrixSource = MATRIX_SUPPLIED;
  }

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

    // class
    result.disableAllClasses();
    result.disableAllClassDependencies();
    result.enable(Capability.NOMINAL_CLASS);
    
    return result;
  }

  /**
   * Builds the model of the base learner.
   *
   * @param data the training data
   * @throws Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

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

    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();
    
    if (m_Classifier == null) {
      throw new Exception("No base classifier has been set!");
    }
    if (m_MatrixSource == MATRIX_ON_DEMAND) {
      String costName = data.relationName() + CostMatrix.FILE_EXTENSION;
      File costFile = new File(getOnDemandDirectory(), costName);
      if (!costFile.exists()) {
        throw new Exception("On-demand cost file doesn't exist: " + costFile);
      }
      setCostMatrix(new CostMatrix(new BufferedReader(
                                   new FileReader(costFile))));
    } else if (m_CostMatrix == null) {
      // try loading an old format cost file
      m_CostMatrix = new CostMatrix(data.numClasses());
      m_CostMatrix.readOldFormat(new BufferedReader(
			       new FileReader(m_CostFile)));
    }

    if (!m_MinimizeExpectedCost) {
      Random random = null;
      if (!(m_Classifier instanceof WeightedInstancesHandler)) {
	random = new Random(m_Seed);
      }
      data = m_CostMatrix.applyCostMatrix(data, random);      
    }
    m_Classifier.buildClassifier(data);
  }

  /**
   * Returns class probabilities. When minimum expected cost approach is chosen,
   * returns probability one for class with the minimum expected misclassification
   * cost. Otherwise it returns the probability distribution returned by
   * the base classifier.
   *
   * @param instance the instance to be classified
   * @return the computed distribution for the given instance
   * @throws Exception if instance could not be classified
   * successfully */
  public double[] distributionForInstance(Instance instance) throws Exception {

    if (!m_MinimizeExpectedCost) {
      return m_Classifier.distributionForInstance(instance);
    }
    double [] pred = m_Classifier.distributionForInstance(instance);
    double [] costs = m_CostMatrix.expectedCosts(pred, instance);
    /*
    for (int i = 0; i < pred.length; i++) {
      System.out.print(pred[i] + " ");
    }
    System.out.println();
    for (int i = 0; i < costs.length; i++) {
      System.out.print(costs[i] + " ");
    }
    System.out.println("\n");
    */

    // This is probably not ideal
    int classIndex = Utils.minIndex(costs);
    for (int i = 0; i  < pred.length; i++) {
      if (i == classIndex) {
	pred[i] = 1.0;
      } else {
	pred[i] = 0.0;
      }
    }
    return pred; 
  }

  /**
   *  Returns the type of graph this classifier
   *  represents.
   *  
   *  @return the type of graph this classifier represents
   */   
  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 string representation of the classifier
   */
  public String toString() {

    if (m_Classifier == null) {
      return "CostSensitiveClassifier: No model built yet.";
    }

    String result = "CostSensitiveClassifier using ";
      if (m_MinimizeExpectedCost) {
	result += "minimized expected misclasification cost\n";
      } else {
	result += "reweighted training instances\n";
      }
      result += "\n" + getClassifierSpec()
	+ "\n\nClassifier Model\n"
	+ m_Classifier.toString()
	+ "\n\nCost Matrix\n"
	+ m_CostMatrix.toString();

    return result;
  }

  /**
   * 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 CostSensitiveClassifier(), argv);
  }
}