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

        output.setBuffer(new StringBuffer());
        output.setHeader(test);
        output.setAttributes("first");
        
    Evaluation eval = new Evaluation(train);
        eval.evaluateModel(filteredClassifier, test, output);
        
        // Convert predictions to CSV
        // Format: inst#, actual, predicted, error, probability, (ID)
        String[] scores = new String[new Double(eval.numInstances()).intValue()];
        double[] probabilities = new double[new Double(eval.numInstances()).intValue()];

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          // Build the classifier
          filteredClassifier.buildClassifier(train);
          
          // Evaluate
          eval.evaluateModel(filteredClassifier, test);
          
          // Add predictions
          AddClassification filter = new AddClassification();
          filter.setClassifier(classifier);
          filter.setOutputClassification(true);

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    // calculate error rate if only root node
    if (expansion==0) {
      m_roots[i].m_isLeaf = true;
      eval = new Evaluation(test[i]);
      eval.evaluateModel(m_roots[i], test[i]);
      if (m_UseErrorRate) expansionError += eval.errorRate();
      else expansionError += eval.rootMeanSquaredError();
      count ++;
    }

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    m_Heuristic, m_UseGini)) {
        m_roots[i] = null; // cannot be expanded
        continue;
      }
      eval = new Evaluation(test[i]);
      eval.evaluateModel(m_roots[i], test[i]);
      if (m_UseErrorRate) expansionError += eval.errorRate();
      else expansionError += eval.rootMeanSquaredError();
      count ++;
    }
  }

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      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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        // learning scheme.
  Instances train = trainData.trainCV(m_NumFolds, j, new Random(1));
  Instances test = trainData.testCV(m_NumFolds, j);
  m_Classifier.buildClassifier(train);
  evaluation.setPriors(train);
  evaluation.evaluateModel(m_Classifier, test);
      }
      double error = evaluation.errorRate();
      if (m_Debug) {
  System.err.println("Cross-validated error rate: " 
         + Utils.doubleToString(error, 6, 4));

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    o_Evaluation = new Evaluation(trainCopy);
    String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
    Classifier oneR = AbstractClassifier.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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    m_NumGenerated = 0;
    double sumOfWeights = train.sumOfWeights();
    for (int j = 0; j < getNumIterations(); j++) {
      performIteration(trainYs, trainFs, probs, trainN, sumOfWeights);
      Evaluation eval = new Evaluation(train);
      eval.evaluateModel(this, test);
      results[j] += eval.correct();
    }
  }
      }

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  sample = trainData.resampleWithWeights(randomInstance, weights);


  // Build and evaluate classifier
  m_Classifiers[m_NumIterationsPerformed].buildClassifier(sample);
  evaluation = new Evaluation(data);
  evaluation.evaluateModel(m_Classifiers[m_NumIterationsPerformed], 
         training);
  epsilon = evaluation.errorRate();
  resamplingIterations++;
      } while (Utils.eq(epsilon, 0) && 
        (resamplingIterations < MAX_NUM_RESAMPLING_ITERATIONS));

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