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

  ((Randomizable) m_Classifiers[m_NumIterationsPerformed]).setSeed(randomInstance.nextInt());
      m_Classifiers[m_NumIterationsPerformed].buildClassifier(trainData);


      // Evaluate the classifier
      evaluation = new Evaluation(data);
      evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], training);
      epsilon = evaluation.errorRate();


      // Stop if error too small or error too big and ignore this model
      if (Utils.grOrEq(epsilon, 0.5) || Utils.eq(epsilon, 0)) {
  if (m_NumIterationsPerformed == 0) {

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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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    evaluation.evaluateModel(currentClassifier, test);
  }
      } else {
  currentClassifier.buildClassifier(train);
  evaluation = new Evaluation(train);
  evaluation.evaluateModel(currentClassifier, test);
      }


      double error = evaluation.errorRate();
      if (m_Debug) {
  System.err.println("Error rate: " + Utils.doubleToString(error, 6, 4)

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  m_right.installLinearModels();
      }
      buildLinearModel(m_indices);
    }
    nodeModelEval = new Evaluation(m_instances);
    nodeModelEval.evaluateModel(m_nodeModel, m_instances);
    m_rootMeanSquaredError = nodeModelEval.rootMeanSquaredError();
    // save space
    if (!m_saveInstances) {
      m_instances = new Instances(m_instances, 0);
    }

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      buildLinearModel(m_indices);
      nodeModelEval = new Evaluation(m_instances);


      // count the constant term as a paramter for a leaf
      // Evaluate the model
      nodeModelEval.evaluateModel(m_nodeModel, m_instances);


      m_rootMeanSquaredError = nodeModelEval.rootMeanSquaredError();
    } else {


      // Prune the left and right subtrees

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      nodeModelEval = new Evaluation(m_instances);


      double rmsModel;
      double adjustedErrorModel;


      nodeModelEval.evaluateModel(m_nodeModel, m_instances);


      rmsModel = nodeModelEval.rootMeanSquaredError();
      adjustedErrorModel = rmsModel 
  * pruningFactor(m_numInstances, 
      m_nodeModel.numParameters() + 1);

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      Evaluation nodeEval = new Evaluation(m_instances);
      double     rmsSubTree;
      double     adjustedErrorNode;
      int   l_params = 0, r_params = 0;


      nodeEval.evaluateModel(this, m_instances);


      rmsSubTree = nodeEval.rootMeanSquaredError();


      if (m_left != null) {
  l_params = m_left.numParameters();

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  test.instance(k).setWeight(-test.instance(k).weight());
  ((NaiveBayesUpdateable)copies[j]).updateClassifier(test.instance(k));
  // reset the weight back to its original value
  test.instance(k).setWeight(-test.instance(k).weight());
      }
      eval.evaluateModel(copies[j], test);
    }
    return eval.incorrect();
  }
  
  /**

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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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     * @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();
    }


    /**
     * Returns the error of the probability estimates for the current model on a set of instances.

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