Examples of weka.classifiers.Evaluation

weka.classifiers.Evaluation
Class for evaluating machine learning models.
-------------------------------------------------------------------
General options when evaluating a learning scheme from the command-line:
-t filename
Name of the file with the training data. (required)
-T filename
Name of the file with the test data. If missing a cross-validation is performed.
-c index
Index of the class attribute (1, 2, ...; default: last).
-x number
The number of folds for the cross-validation (default: 10).
-no-cv
No cross validation. If no test file is provided, no evaluation is done.
-split-percentage percentage
Sets the percentage for the train/test set split, e.g., 66.
-preserve-order
Preserves the order in the percentage split instead of randomizing the data first with the seed value ('-s').
-s seed
Random number seed for the cross-validation and percentage split (default: 1).
-m filename
The name of a file containing a cost matrix.
-l filename
Loads classifier from the given file. In case the filename ends with ".xml", a PMML file is loaded or, if that fails, options are loaded from XML.
-d filename
Saves classifier built from the training data into the given file. In case the filename ends with ".xml" the options are saved XML, not the model.
-v
Outputs no statistics for the training data.
-o
Outputs statistics only, not the classifier.
-i
Outputs information-retrieval statistics per class.
-k
Outputs information-theoretic statistics.
-classifications "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
Uses the specified class for generating the classification output. E.g.: weka.classifiers.evaluation.output.prediction.PlainText or : weka.classifiers.evaluation.output.prediction.CSV -p range
Outputs predictions for test instances (or the train instances if no test instances provided and -no-cv is used), along with the attributes in the specified range (and nothing else). Use '-p 0' if no attributes are desired.
Deprecated: use "-classifications ..." instead.
-distribution
Outputs the distribution instead of only the prediction in conjunction with the '-p' option (only nominal classes).
Deprecated: use "-classifications ..." instead.
-r
Outputs cumulative margin distribution (and nothing else).
-g
Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).
-xml filename | xml-string
Retrieves the options from the XML-data instead of the command line.
-threshold-file file
The file to save the threshold data to. The format is determined by the extensions, e.g., '.arff' for ARFF format or '.csv' for CSV.
-threshold-label label
The class label to determine the threshold data for (default is the first label)
-------------------------------------------------------------------
Example usage as the main of a classifier (called FunkyClassifier):
```
 public static void main(String [] args) { runClassifier(new FunkyClassifier(), args); } 
```
------------------------------------------------------------------
Example usage from within an application:
```
 Instances trainInstances = ... instances got from somewhere Instances testInstances = ... instances got from somewhere Classifier scheme = ... scheme got from somewhere Evaluation evaluation = new Evaluation(trainInstances); evaluation.evaluateModel(scheme, testInstances); System.out.println(evaluation.toSummaryString()); 
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 7228 $

  boolean prune = (nodeList.size() > 0);               


  //alpha_0 is always zero (unpruned tree)
  alphas[0] = 0;


  Evaluation eval;


  //error of unpruned tree
  if (errors != null) {
      eval = new Evaluation(test);
      eval.evaluateModel(this, test);
      errors[0] = eval.errorRate(); 
  }  
       
  int iteration = 0;
  while (prune) {


      iteration++;
      
      //get node with minimum alpha
      LMTNode nodeToPrune = (LMTNode)Collections.min(nodeList,comparator);


      nodeToPrune.m_isLeaf = true;
      //Do not set m_sons null, want to unprune
      
      //get alpha-value of node
      alphas[iteration] = nodeToPrune.m_alpha;
       
      //log error
      if (errors != null) {
    eval = new Evaluation(test);
    eval.evaluateModel(this, test);
    errors[iteration] = eval.errorRate(); 
      }


      //update errors/alphas
      treeErrors();
      calculateAlphas();

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    /**
     *Updates the numIncorrectModel field for all nodes. This is needed for calculating the alpha-values. 
     */
    public void modelErrors() throws Exception{
    
  Evaluation eval = new Evaluation(m_train);
    
  if (!m_isLeaf) {
      m_isLeaf = true;
      eval.evaluateModel(this, m_train);
      m_isLeaf = false;
      m_numIncorrectModel = eval.incorrect();
      for (int i = 0; i < m_sons.length; i++) m_sons[i].modelErrors();
  } else {
      eval.evaluateModel(this, m_train);
      m_numIncorrectModel = eval.incorrect();
  }
    }

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     * @param data the set of instances
     * @return the error rate
     * @throws Exception if something goes wrong
     */
    protected double getErrorRate(Instances data) throws Exception {
  Evaluation eval = new Evaluation(data);
  eval.evaluateModel(this,data);
  return eval.errorRate();
    }

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     * @param data the set of instances
     * @return the error
     * @throws Exception if something goes wrong
     */
    protected double getMeanAbsoluteError(Instances data) throws Exception {
  Evaluation eval = new Evaluation(data);
  eval.evaluateModel(this,data);
  return eval.meanAbsoluteError();
    }

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    public void crossValidate() throws Exception {
        // stratify nominal target class
        if (_trainInstances.classAttribute().isNominal()) {
            _trainInstances.stratify(_folds);
        }
        _eval = new Evaluation(_trainInstances);
        for (int n = 0; n < _folds; n++) {


            if (_logger.isDebugEnabled()) {
                _logger.debug("Cross validation fold: " + (n + 1));
            }

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     *             If model can not be evaluated
     */
    public void errorAnalysis(String attribute) throws Exception {
        readModel();
        _logger.info("Performing error analysis");
        Evaluation eval = new Evaluation(_testInstances);
        eval.evaluateModel(_cls, _testInstances);
        _predictionList = new HashMap<String, List<Prediction>>();
        String predicted, actual = null;
        double[] distribution = null;
        _predictionList.put(_config._truePositives, new ArrayList<Prediction>());
        _predictionList.put(_config._trueNegatives, new ArrayList<Prediction>());

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     *             If model can not be evaluated.
     */
    public void evaluate() throws Exception {
        readModel();
        _logger.info("Classifying with " + _config._classifier);
        Evaluation eval = new Evaluation(_testInstances);
        eval.evaluateModel(_cls, _testInstances);
        _logger.info("\n" + eval.toSummaryString());
        try {
            _logger.info("\n" + eval.toClassDetailsString());
        } catch (Exception e) {
            _logger.info("Can not create class details" + _config._classifier);
        }
        try {
            _logger.info("\n" + _eval.toMatrixString());

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      // Randomize the data
      data.randomize(random);
    
      // Perform cross-validation
        Instances predictedData = null;
        Evaluation eval = new Evaluation(data);
        
        for (int n = 0; n < folds; n++)
        {
          Instances train = data.trainCV(folds, n, random);
            Instances test = data.testCV(folds, n);
            
            // Apply log filter
          Filter logFilter = new LogFilter();
            logFilter.setInputFormat(train);
            train = Filter.useFilter(train, logFilter);        
            logFilter.setInputFormat(test);
            test = Filter.useFilter(test, logFilter);
            
            // Copy the classifier
            Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);
                                   
            // Instantiate the FilteredClassifier
            FilteredClassifier filteredClassifier = new FilteredClassifier();
            filteredClassifier.setFilter(removeIDFilter);
            filteredClassifier.setClassifier(classifier);
               
            // Build the classifier
            filteredClassifier.buildClassifier(train);
             
            // Evaluate
            eval.evaluateModel(classifier, test);
            
            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);

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    // Randomize the data
    data.randomize(random);
  
    // Perform cross-validation
      Instances predictedData = null;
      Evaluation eval = new Evaluation(data);
      
      for (int n = 0; n < folds; n++)
      {
        Instances train = data.trainCV(folds, n, random);
          Instances test = data.testCV(folds, n);
          
          // Apply log filter
//        Filter logFilter = new LogFilter();
//          logFilter.setInputFormat(train);
//          train = Filter.useFilter(train, logFilter);        
//          logFilter.setInputFormat(test);
//          test = Filter.useFilter(test, logFilter);
          
          // Copy the classifier
          Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);
                                 
          // Instantiate the FilteredClassifier
          FilteredClassifier filteredClassifier = new FilteredClassifier();
          filteredClassifier.setFilter(removeIDFilter);
          filteredClassifier.setClassifier(classifier);
             
          // Build the classifier
          filteredClassifier.buildClassifier(train);
           
          // Evaluate
          eval.evaluateModel(filteredClassifier, test);
          
          // Add predictions
          AddClassification filter = new AddClassification();
          filter.setClassifier(filteredClassifier);
          filter.setOutputClassification(true);

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      // Randomize the data
      data.randomize(random);
    
      // Perform cross-validation
        Instances predictedData = null;
        Evaluation eval = new Evaluation(data);
        
        for (int n = 0; n < folds; n++)
        {
          Instances train = data.trainCV(folds, n, random);
            Instances test = data.testCV(folds, n);
            
            // Apply log filter
          Filter logFilter = new LogFilter();
            logFilter.setInputFormat(train);
            train = Filter.useFilter(train, logFilter);        
            logFilter.setInputFormat(test);
            test = Filter.useFilter(test, logFilter);
            
            // Copy the classifier
            Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);
                                   
            // Instantiate the FilteredClassifier
            FilteredClassifier filteredClassifier = new FilteredClassifier();
            filteredClassifier.setFilter(removeIDFilter);
            filteredClassifier.setClassifier(classifier);
               
            // Build the classifier
            filteredClassifier.buildClassifier(train);
             
            // Evaluate
            eval.evaluateModel(classifier, test);
            
            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);

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