Examples of weka.classifiers.Evaluation.evaluateModel()

weka.classifiers.Evaluation.evaluateModel()
Evaluates a classifier with the options given in an array of strings.
Valid options are:
-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 detailed 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)
@param classifierString class of machine learning classifier as a string @param options the array of string containing the options @throws Exception if model could not be evaluated successfully @return a string describing the results

      for (int i = 0; i < m_numFoldsPruning; i++) {
  modelError[i] = new FastVector();


  m_roots[i].m_isLeaf = true;
  Evaluation eval = new Evaluation(test[i]);
  eval.evaluateModel(m_roots[i], test[i]);
  double error;
  if (m_UseErrorRate) error = eval.errorRate();
  else error = eval.rootMeanSquaredError();
  modelError[i].addElement(new Double(error));

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  for (int i=0; i<2; i++){
    m_Successors[i].makeLeaf(train);
  }


  Evaluation eval = new Evaluation(test);
  eval.evaluateModel(root, test);
  double error;
  if (useErrorRate) error = eval.errorRate();
  else error = eval.rootMeanSquaredError();
  modelError.addElement(new Double(error));
      }

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    o_Evaluation = new Evaluation(trainCopy);
    String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
    Classifier oneR = Classifier.forName("weka.classifiers.rules.OneR", oneROpts);
    if (m_evalUsingTrainingData) {
      oneR.buildClassifier(trainCopy);
      o_Evaluation.evaluateModel(oneR, trainCopy);
    } else {
      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */
      o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed));

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    //testing classifier
    testTimeStart = System.currentTimeMillis();
    if(canMeasureCPUTime) 
      CPUStartTime = thMonitor.getThreadUserTime(thID);
    predictions = eval.evaluateModel(m_Classifier, test);
    if(canMeasureCPUTime)
      testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    testTimeElapsed = System.currentTimeMillis() - testTimeStart;
    thMonitor = null;

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      trainCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
    testTimeStart = System.currentTimeMillis();
    if(canMeasureCPUTime)
      CPUStartTime = thMonitor.getThreadUserTime(thID);
    eval.evaluateModel(m_Classifier, test);
    if(canMeasureCPUTime)
      testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    testTimeElapsed = System.currentTimeMillis() - testTimeStart;
    thMonitor = null;

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    Evaluation eval;


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


    int iteration = 0;
    double preAlpha = Double.MAX_VALUE;

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      alphas[iteration] = nodeToPrune.m_Alpha;


      // log error
      if (errors != null) {
  eval = new Evaluation(test);
  eval.evaluateModel(this, test);
  errors[iteration] = eval.errorRate();
      }
      preAlpha = nodeToPrune.m_Alpha;


      //update errors/alphas

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    if (!m_isLeaf) {
      m_isLeaf = true; //temporarily make leaf


      // calculate distribution for evaluation
      eval.evaluateModel(this, m_train);
      m_numIncorrectModel = eval.incorrect();


      m_isLeaf = false;


      for (int i = 0; i < m_Successors.length; i++)

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      for (int i = 0; i < m_Successors.length; i++)
  m_Successors[i].modelErrors();


    } else {
      eval.evaluateModel(this, m_train);
      m_numIncorrectModel = eval.incorrect();
    }       
  }


  /**

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          // learning scheme.
    train = newData.trainCV(m_NumXValFolds, j, new Random (1));
    test = newData.testCV(m_NumXValFolds, j);
    currentClassifier.buildClassifier(train);
    evaluation.setPriors(train);
    evaluation.evaluateModel(currentClassifier, test);
  }
      } else {
  currentClassifier.buildClassifier(train);
  evaluation = new Evaluation(train);
  evaluation.evaluateModel(currentClassifier, test);

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