evaluation.java

一个数据挖掘系统的源码

       throws Exception {

    crossValidateModel(Classifier.forName(classifierString, options),
		       data, numFolds);
  }

  /**
   * Evaluates a classifier with the options given in an array of
   * strings. <p>
   *
   * Valid options are: <p>
   *
   * -t filename <br>
   * Name of the file with the training data. (required) <p>
   *
   * -T filename <br>
   * Name of the file with the test data. If missing a cross-validation
   * is performed. <p>
   *
   * -c index <br>
   * Index of the class attribute (1, 2, ...; default: last). <p>
   *
   * -x number <br>
   * The number of folds for the cross-validation (default: 10). <p>
   *
   * -s seed <br>
   * Random number seed for the cross-validation (default: 1). <p>
   *
   * -m filename <br>
   * The name of a file containing a cost matrix. <p>
   *
   * -l filename <br>
   * Loads classifier from the given file. <p>
   *
   * -d filename <br>
   * Saves classifier built from the training data into the given file. <p>
   *
   * -v <br>
   * Outputs no statistics for the training data. <p>
   *
   * -o <br>
   * Outputs statistics only, not the classifier. <p>
   *
   * -i <br>
   * Outputs detailed information-retrieval statistics per class. <p>
   *
   * -k <br>
   * Outputs information-theoretic statistics. <p>
   *
   * -p range <br>
   * Outputs predictions for test instances, along with the attributes in
   * the specified range (and nothing else). Use '-p 0' if no attributes are
   * desired. <p>
   *
   * -r <br>
   * Outputs cumulative margin distribution (and nothing else). <p>
   *
   * -g <br>
   * Only for classifiers that implement "Graphable." Outputs
   * the graph representation of the classifier (and nothing
   * else). <p>
   *
   * @param classifierString class of machine learning classifier as a string
   * @param options the array of string containing the options
   * @exception Exception if model could not be evaluated successfully
   * @return a string describing the results
   */
  public static String evaluateModel(String classifierString,
				     String [] options) throws Exception {

    Classifier classifier;

    // Create classifier
    try {
      classifier =
      (Classifier)Class.forName(classifierString).newInstance();
    } catch (Exception e) {
      throw new Exception("Can't find class with name "
			  + classifierString + '.');
    }
    return evaluateModel(classifier, options);
  }

  /**
   * A test method for this class. Just extracts the first command line
   * argument as a classifier class name and calls evaluateModel.
   * @param args an array of command line arguments, the first of which
   * must be the class name of a classifier.
   */
/*
  public static void main(String [] args) {

    try {
      if (args.length == 0) {
	throw new Exception("The first argument must be the class name"
2			    + " of a classifier");
      }
      String classifier = args[0];
      args[0] = "";
      System.out.println(evaluateModel(classifier, args));
    } catch (Exception ex) {
      ex.printStackTrace();
      System.err.println(ex.getMessage());
    }
  }
*/
  /**
   * Evaluates a classifier with the options given in an array of
   * strings. <p>
   *
   * Valid options are: <p>
   *
   * -t name of training file <br>
   * Name of the file with the training data. (required) <p>
   *
   * -T name of test file <br>
   * Name of the file with the test data. If missing a cross-validation
   * is performed. <p>
   *
   * -c class index <br>
   * Index of the class attribute (1, 2, ...; default: last). <p>
   *
   * -x number of folds <br>
   * The number of folds for the cross-validation (default: 10). <p>
   *
   * -s random number seed <br>
   * Random number seed for the cross-validation (default: 1). <p>
   *
   * -m file with cost matrix <br>
   * The name of a file containing a cost matrix. <p>
   *
   * -l name of model input file <br>
   * Loads classifier from the given file. <p>
   *
   * -d name of model output file <br>
   * Saves classifier built from the training data into the given file. <p>
   *
   * -v <br>
   * Outputs no statistics for the training data. <p>
   *
   * -o <br>
   * Outputs statistics only, not the classifier. <p>
   *
   * -i <br>
   * Outputs detailed information-retrieval statistics per class. <p>
   *
   * -k <br>
   * Outputs information-theoretic statistics. <p>
   *
   * -p <br>
   * Outputs predictions for test instances (and nothing else). <p>
   *
   * -r <br>
   * Outputs cumulative margin distribution (and nothing else). <p>
   *
   * -g <br>
   * Only for classifiers that implement "Graphable." Outputs
   * the graph representation of the classifier (and nothing
   * else). <p>
   *
   * @param classifier machine learning classifier
   * @param options the array of string containing the options
   * @exception Exception if model could not be evaluated successfully
   * @return a string describing the results */

  public static String evaluateModel(Classifier classifier,
				     String [] options) throws Exception {

    Instances train = null, tempTrain, test = null, template = null;
    int seed = 1, folds = 10, classIndex = -1;
    String trainFileName, testFileName, sourceClass,
      classIndexString, seedString, foldsString, objectInputFileName,
      objectOutputFileName, attributeRangeString;
    boolean IRstatistics = false, noOutput = false,
      printClassifications = false, trainStatistics = true,
      printMargins = false, printComplexityStatistics = false,
      printGraph = false, classStatistics = false, printSource = false;
    StringBuffer text = new StringBuffer();
    BufferedReader trainReader = null, testReader = null;
    ObjectInputStream objectInputStream = null;
    Random random;
    CostMatrix costMatrix = null;
    StringBuffer schemeOptionsText = null;
    Range attributesToOutput = null;
    long trainTimeStart = 0, trainTimeElapsed = 0,
      testTimeStart = 0, testTimeElapsed = 0;

    try {

      // Get basic options (options the same for all schemes)
      classIndexString = Utils.getOption('c', options);
      if (classIndexString.length() != 0) {
	classIndex = Integer.parseInt(classIndexString);
      }
      trainFileName = Utils.getOption('t', options);
      objectInputFileName = Utils.getOption('l', options);
      objectOutputFileName = Utils.getOption('d', options);
      testFileName = Utils.getOption('T', options);
      if (trainFileName.length() == 0) {
	if (objectInputFileName.length() == 0) {
	  throw new Exception("No training file and no object "+
			      "input file given.");
	}
	if (testFileName.length() == 0) {
	  throw new Exception("No training file and no test "+
			      "file given.");
	}
      } else if ((objectInputFileName.length() != 0) &&
		 ((!(classifier instanceof UpdateableClassifier)) ||
		 (testFileName.length() == 0))) {
	throw new Exception("Classifier not incremental, or no " +
			    "test file provided: can't "+
			    "use both train and model file.");
      }
      try {
	if (trainFileName.length() != 0) {
	  trainReader = new BufferedReader(new FileReader(trainFileName));
	}
	if (testFileName.length() != 0) {
	  testReader = new BufferedReader(new FileReader(testFileName));
	}
	if (objectInputFileName.length() != 0) {
          InputStream is = new FileInputStream(objectInputFileName);
          if (objectInputFileName.endsWith(".gz")) {
            is = new GZIPInputStream(is);
          }
	  objectInputStream = new ObjectInputStream(is);
	}
      }
      catch (Exception e) {
	throw new Exception("Can't open file " + e.getMessage() + '.');
      }
      if (testFileName.length() != 0) {
	template = test = new Instances(testReader, 1);
	if (classIndex != -1) {
	  test.setClassIndex(classIndex - 1);
	}
        else {
	  test.setClassIndex(test.numAttributes() - 1);
	}
	if (classIndex > test.numAttributes()) {
	  throw new Exception("Index of class attribute too large.");
	}
      }
      if (trainFileName.length() != 0) {
	if ((classifier instanceof UpdateableClassifier) &&
	    (testFileName.length() != 0)) {
	  train = new Instances(trainReader, 1);
	}
        else {
	  train = new Instances(trainReader);
	}
        template = train;
	if (classIndex != -1) {
	  train.setClassIndex(classIndex - 1);
	}
        else {
	  train.setClassIndex(train.numAttributes() - 1);
	}
	if (classIndex > train.numAttributes()) {
	  throw new Exception("Index of class attribute too large.");
	}
	//train = new Instances(train);
      }
      if (template == null) {
        throw new Exception("No actual dataset provided to use as template");
      }
      seedString = Utils.getOption('s', options);
      if (seedString.length() != 0) {
	seed = Integer.parseInt(seedString);
      }
      foldsString = Utils.getOption('x', options);
      if (foldsString.length() != 0) {
	folds = Integer.parseInt(foldsString);
      }
      costMatrix = handleCostOption(Utils.getOption('m', options), template.numClasses());

      classStatistics = Utils.getFlag('i', options);
      noOutput = Utils.getFlag('o', options);
      trainStatistics = !Utils.getFlag('v', options);
      printComplexityStatistics = Utils.getFlag('k', options);
      printMargins = Utils.getFlag('r', options);
      printGraph = Utils.getFlag('g', options);
      sourceClass = Utils.getOption('z', options);
      printSource = (sourceClass.length() != 0);

      // Check -p option
      try {
	attributeRangeString = Utils.getOption('p', options);
      }
      catch (Exception e) {
	throw new Exception(e.getMessage() + "\nNOTE: the -p option has changed. " +
			    "It now expects a parameter specifying a range of attributes " +
			    "to list with the predictions. Use '-p 0' for none.");
      }
      if (attributeRangeString.length() != 0) {
	printClassifications = true;
	if (!attributeRangeString.equals("0"))
	  attributesToOutput = new Range(attributeRangeString);
      }

      // If a model file is given, we can't process
      // scheme-specific options
      if (objectInputFileName.length() != 0) {
	Utils.checkForRemainingOptions(options);
      }
      else {
	// Set options for classifier
	if (classifier instanceof OptionHandler) {
	  for (int i = 0; i < options.length; i++) {
	    if (options[i].length() != 0) {
	      if (schemeOptionsText == null) {
		schemeOptionsText = new StringBuffer();
	      }
	      if (options[i].indexOf(' ') != -1) {
		schemeOptionsText.append('"' + options[i] + "\" ");
	      } else {
		schemeOptionsText.append(options[i] + " ");
	      }
	    }
	  }
	  ((OptionHandler)classifier).setOptions(options);
	}
      }
      Utils.checkForRemainingOptions(options);
    }
    catch (Exception e) {
      throw new Exception("\nWeka exception: " + e.getMessage()
			   + makeOptionString(classifier));
    }


    // Setup up evaluation objects
    Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
    Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);

    if (objectInputFileName.length() != 0) {

      // Load classifier from file
      classifier = (Classifier) objectInputStream.readObject();
      objectInputStream.close();
    }

    // Build the classifier if no object file provided
    if ((classifier instanceof UpdateableClassifier) &&
	(testFileName.length() != 0) &&
	(costMatrix == null) &&
	(trainFileName.length() != 0)) {

      // Build classifier incrementally
      trainingEvaluation.setPriors(train);
      testingEvaluation.setPriors(train);
      trainTimeStart = System.currentTimeMillis();
      if (objectInputFileName.length() == 0) {
	classifier.buildClassifier(train);
      }
      while (train.readInstance(trainReader)) {
	trainingEvaluation.updatePriors(train.instance(0));
	testingEvaluation.updatePriors(train.instance(0));
	((UpdateableClassifier)classifier).
	  updateClassifier(train.instance(0));
	train.delete(0);
      }
      trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
      trainReader.close();
    } else if (objectInputFileName.length() == 0) {

      // Build classifier in one go
      tempTrain = new Instances(train);
      trainingEvaluation.setPriors(tempTrain);
      testingEvaluation.setPriors(tempTrain);
      trainTimeStart = System.currentTimeMillis();
      classifier.buildClassifier(tempTrain);
      trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
    }