ensembleselection.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

  /**
   * We return true for basically everything except for Missing class values,
   * because we can't really answer for all the models in our library. If any of
   * them don't work with the supplied data then we just trap the exception.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities(); // returns the object
    // from
    // weka.classifiers.Classifier
    
    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);
    result.enable(Capability.BINARY_ATTRIBUTES);
    
    // class
    result.enable(Capability.NOMINAL_CLASS);
    result.enable(Capability.NUMERIC_CLASS);
    result.enable(Capability.BINARY_CLASS);
    
    return result;
  }
  
  /**
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -L &lt;/path/to/modelLibrary&gt;
   *  Specifies the Model Library File, continuing the list of all models.</pre>
   * 
   * <pre> -W &lt;/path/to/working/directory&gt;
   *  Specifies the Working Directory, where all models will be stored.</pre>
   * 
   * <pre> -B &lt;numModelBags&gt;
   *  Set the number of bags, i.e., number of iterations to run 
   *  the ensemble selection algorithm.</pre>
   * 
   * <pre> -E &lt;modelRatio&gt;
   *  Set the ratio of library models that will be randomly chosen 
   *  to populate each bag of models.</pre>
   * 
   * <pre> -V &lt;validationRatio&gt;
   *  Set the ratio of the training data set that will be reserved 
   *  for validation.</pre>
   * 
   * <pre> -H &lt;hillClimbIterations&gt;
   *  Set the number of hillclimbing iterations to be performed 
   *  on each model bag.</pre>
   * 
   * <pre> -I &lt;sortInitialization&gt;
   *  Set the the ratio of the ensemble library that the sort 
   *  initialization algorithm will be able to choose from while 
   *  initializing the ensemble for each model bag</pre>
   * 
   * <pre> -X &lt;numFolds&gt;
   *  Sets the number of cross-validation folds.</pre>
   * 
   * <pre> -P &lt;hillclimbMettric&gt;
   *  Specify the metric that will be used for model selection 
   *  during the hillclimbing algorithm.
   *  Valid metrics are: 
   *   accuracy, rmse, roc, precision, recall, fscore, all</pre>
   * 
   * <pre> -A &lt;algorithm&gt;
   *  Specifies the algorithm to be used for ensemble selection. 
   *  Valid algorithms are:
   *   "forward" (default) for forward selection.
   *   "backward" for backward elimination.
   *   "both" for both forward and backward elimination.
   *   "best" to simply print out top performer from the 
   *      ensemble library
   *   "library" to only train the models in the ensemble 
   *      library</pre>
   * 
   * <pre> -R
   *  Flag whether or not models can be selected more than once 
   *  for an ensemble.</pre>
   * 
   * <pre> -G
   *  Whether sort initialization greedily stops adding models 
   *  when performance degrades.</pre>
   * 
   * <pre> -O
   *  Flag for verbose output. Prints out performance of all 
   *  selected models.</pre>
   * 
   * <pre> -S &lt;num&gt;
   *  Random number seed.
   *  (default 1)</pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</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	tmpStr;
    
    tmpStr = Utils.getOption('L', options);
    if (tmpStr.length() != 0) {
      m_modelLibraryFileName = tmpStr;
      m_library = new EnsembleSelectionLibrary(m_modelLibraryFileName);
    } else {
      setLibrary(new EnsembleSelectionLibrary());
      // setLibrary(new Library(super.m_Classifiers));
    }
    
    tmpStr = Utils.getOption('W', options);
    if (tmpStr.length() != 0 && validWorkingDirectory(tmpStr)) {
      m_workingDirectory = new File(tmpStr);
    } else {
      m_workingDirectory = new File(getDefaultWorkingDirectory());
    }
    m_library.setWorkingDirectory(m_workingDirectory);
    
    tmpStr = Utils.getOption('E', options);
    if (tmpStr.length() != 0) {
      setModelRatio(Double.parseDouble(tmpStr));
    } else {
      setModelRatio(1.0);
    }
    
    tmpStr = Utils.getOption('V', options);
    if (tmpStr.length() != 0) {
      setValidationRatio(Double.parseDouble(tmpStr));
    } else {
      setValidationRatio(0.25);
    }
    
    tmpStr = Utils.getOption('B', options);
    if (tmpStr.length() != 0) {
      setNumModelBags(Integer.parseInt(tmpStr));
    } else {
      setNumModelBags(10);
    }
    
    tmpStr = Utils.getOption('H', options);
    if (tmpStr.length() != 0) {
      setHillclimbIterations(Integer.parseInt(tmpStr));
    } else {
      setHillclimbIterations(100);
    }
    
    tmpStr = Utils.getOption('I', options);
    if (tmpStr.length() != 0) {
      setSortInitializationRatio(Double.parseDouble(tmpStr));
    } else {
      setSortInitializationRatio(1.0);
    }
    
    tmpStr = Utils.getOption('X', options);
    if (tmpStr.length() != 0) {
      setNumFolds(Integer.parseInt(tmpStr));
    } else {
      setNumFolds(10);
    }
    
    setReplacement(Utils.getFlag('R', options));
    
    setGreedySortInitialization(Utils.getFlag('G', options));
    
    setVerboseOutput(Utils.getFlag('O', options));
    
    tmpStr = Utils.getOption('P', options);
    // if (hillclimbMetricString.length() != 0) {
    
    if (tmpStr.toLowerCase().equals("accuracy")) {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_ACCURACY, TAGS_METRIC));
    } else if (tmpStr.toLowerCase().equals("rmse")) {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_RMSE, TAGS_METRIC));
    } else if (tmpStr.toLowerCase().equals("roc")) {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_ROC, TAGS_METRIC));
    } else if (tmpStr.toLowerCase().equals("precision")) {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_PRECISION, TAGS_METRIC));
    } else if (tmpStr.toLowerCase().equals("recall")) {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_RECALL, TAGS_METRIC));
    } else if (tmpStr.toLowerCase().equals("fscore")) {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_FSCORE, TAGS_METRIC));
    } else if (tmpStr.toLowerCase().equals("all")) {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_ALL, TAGS_METRIC));
    } else {
      setHillclimbMetric(new SelectedTag(
	  EnsembleMetricHelper.METRIC_RMSE, TAGS_METRIC));
    }
    
    tmpStr = Utils.getOption('A', options);
    if (tmpStr.toLowerCase().equals("forward")) {
      setAlgorithm(new SelectedTag(ALGORITHM_FORWARD, TAGS_ALGORITHM));
    } else if (tmpStr.toLowerCase().equals("backward")) {
      setAlgorithm(new SelectedTag(ALGORITHM_BACKWARD, TAGS_ALGORITHM));
    } else if (tmpStr.toLowerCase().equals("both")) {
      setAlgorithm(new SelectedTag(ALGORITHM_FORWARD_BACKWARD, TAGS_ALGORITHM));
    } else if (tmpStr.toLowerCase().equals("forward")) {
      setAlgorithm(new SelectedTag(ALGORITHM_FORWARD, TAGS_ALGORITHM));
    } else if (tmpStr.toLowerCase().equals("best")) {
      setAlgorithm(new SelectedTag(ALGORITHM_BEST, TAGS_ALGORITHM));
    } else if (tmpStr.toLowerCase().equals("library")) {
      setAlgorithm(new SelectedTag(ALGORITHM_BUILD_LIBRARY, TAGS_ALGORITHM));
    } else {
      setAlgorithm(new SelectedTag(ALGORITHM_FORWARD, TAGS_ALGORITHM));
    }
    
    super.setOptions(options);
    
    m_library.setDebug(m_Debug);
  }
  
  
  /**
   * Gets the current settings of the Classifier.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {
    Vector        result;
    String[]      options;
    int           i;
    
    result  = new Vector();
    
    if (m_library.getModelListFile() != null) {
      result.add("-L");
      result.add("" + m_library.getModelListFile());
    }
    
    if (!m_workingDirectory.equals("")) {
      result.add("-W");
      result.add("" + getWorkingDirectory());
    }
    
    result.add("-P");
    switch (getHillclimbMetric().getSelectedTag().getID()) {
      case (EnsembleMetricHelper.METRIC_ACCURACY):
	result.add("accuracy");
      break;
      case (EnsembleMetricHelper.METRIC_RMSE):
	result.add("rmse");
      break;
      case (EnsembleMetricHelper.METRIC_ROC):
	result.add("roc");
      break;
      case (EnsembleMetricHelper.METRIC_PRECISION):
	result.add("precision");
      break;
      case (EnsembleMetricHelper.METRIC_RECALL):
	result.add("recall");
      break;
      case (EnsembleMetricHelper.METRIC_FSCORE):
	result.add("fscore");
      break;
      case (EnsembleMetricHelper.METRIC_ALL):
	result.add("all");
      break;
    }
    
    result.add("-A");
    switch (getAlgorithm().getSelectedTag().getID()) {
      case (ALGORITHM_FORWARD):
	result.add("forward");
      break;
      case (ALGORITHM_BACKWARD):
	result.add("backward");
      break;
      case (ALGORITHM_FORWARD_BACKWARD):
	result.add("both");
      break;
      case (ALGORITHM_BEST):
	result.add("best");
      break;
      case (ALGORITHM_BUILD_LIBRARY):
	result.add("library");
      break;
    }
    
    result.add("-B");
    result.add("" + getNumModelBags());
    result.add("-V");
    result.add("" + getValidationRatio());
    result.add("-E");
    result.add("" + getModelRatio());
    result.add("-H");
    result.add("" + getHillclimbIterations());
    result.add("-I");
    result.add("" + getSortInitializationRatio());
    result.add("-X");
    result.add("" + getNumFolds());
    
    if (m_replacement)
      result.add("-R");
    if (m_greedySortInitialization)
      result.add("-G");
    if (m_verboseOutput)
      result.add("-O");
    
    options = super.getOptions();
    for (i = 0; i < options.length; i++)
      result.add(options[i]);
    
    return (String[]) result.toArray(new String[result.size()]);
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numFoldsTipText() {
    return "The number of folds used for cross-validation.";
  }
  
  /**
   * Gets the number of folds for the cross-validation.
   * 
   * @return the number of folds for the cross-validation
   */
  public int getNumFolds() {
    return m_NumFolds;
  }
  
  /**
   * Sets the number of folds for the cross-validation.
   * 
   * @param numFolds
   *            the number of folds for the cross-validation
   * @throws Exception
   *                if parameter illegal
   */
  public void setNumFolds(int numFolds) throws Exception {
    if (numFolds < 0) {
      throw new IllegalArgumentException(
	  "EnsembleSelection: Number of cross-validation "
	  + "folds must be positive.");
    }
    m_NumFolds = numFolds;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String libraryTipText() {
    return "An ensemble library.";
  }
  
  /**
   * Gets the ensemble library.
   * 
   * @return the ensemble library
   */
  public EnsembleSelectionLibrary getLibrary() {
    return m_library;
  }
  
  /**
   * Sets the ensemble library.
   * 
   * @param newLibrary
   *            the ensemble library
   */
  public void setLibrary(EnsembleSelectionLibrary newLibrary) {
    m_library = newLibrary;
    m_library.setDebug(m_Debug);
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String modelRatioTipText() {
    return "The ratio of library models that will be randomly chosen to be used for each iteration.";
  }
  
  /**
   * Get the value of modelRatio.
   * 
   * @return Value of modelRatio.
   */
  public double getModelRatio() {
    return m_modelRatio;
  }
  
  /**
   * Set the value of modelRatio.
   * 
   * @param v
   *            Value to assign to modelRatio.
   */
  public void setModelRatio(double v) {
    m_modelRatio = v;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String validationRatioTipText() {
    return "The ratio of the training data set that will be reserved for validation.";
  }
  
  /**
   * Get the value of validationRatio.
   * 
   * @return Value of validationRatio.
   */
  public double getValidationRatio() {
    return m_validationRatio;
  }
  
  /**
   * Set the value of validationRatio.
   * 
   * @param v
   *            Value to assign to validationRatio.
   */
  public void setValidationRatio(double v) {
    m_validationRatio = v;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui