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

     * @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();
    }


    /**
     * Helper function to find the minimum in an array of error values.

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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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    //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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                    // use 5-fold cross-validation
                    ev.crossValidateModel(classifier, train, Math.min(train.numInstances(), 5), new Random(42));
                } else {
                    test.setClassIndex(1);
                    classifier.buildClassifier(train);
                    ev.evaluateModel(classifier, test);
                }
                
                m_ROCs[snpNum] = ev.weightedAreaUnderROC();


                if (snpNum % 1000 == 0) {

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                        }






                            Evaluation eval = new Evaluation(WinningShuffle_Learn);
                            eval.evaluateModel(Classifier.makeCopy(cpyCls[bestIndex]), WinningShuffle_Hold);


                            //////Send to another sub for report generating
//                            generateReport(learn, hold, WinningShuffle_Learn, WinningShuffle_Hold, cpyCls[bestIndex], eval);
                            PhaseIIwrep.res_pctCorrect_valid = eval.pctCorrect();
                            PhaseIIwrep.res_ROC_valid = eval.weightedAreaUnderROC();

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    private void validationWinner(Instances randData,Instances validShuff, Classifier currentClassifier) throws Exception{
        Evaluation eval_validationE = new Evaluation(randData);
        Classifier eval_clas = Classifier.makeCopy(currentClassifier);
        eval_clas.buildClassifier(randData);
        eval_validationE.evaluateModel(eval_clas, validShuff);


        PhaseIwrep.res_pctCorrect_valid = eval_validationE.pctCorrect();
        PhaseIwrep.res_ROC_valid = eval_validationE.weightedAreaUnderROC();


        PhaseIwrep.PII_scoreTypeUsed = ((K2) ((BayesNet) currentClassifier).getSearchAlgorithm()).getScoreType().getSelectedTag().getID();

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            insample_ROC[0] = loggy_eval.weightedAreaUnderROC();
        
        Evaluation loggy_eval_validation = new Evaluation(learn);
        Classifier loggy_valid = Classifier.makeCopy(loggy_class);
        loggy_valid.buildClassifier(learn);
        loggy_eval_validation.evaluateModel(loggy_valid, hold);
        validation_ACC[0] = loggy_eval_validation.pctCorrect();
        validation_ROC[0] = loggy_eval_validation.weightedAreaUnderROC();
        
        // DECISION TABLE
        Classifier dtable_class = new DecisionTable();

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            insample_ROC[1] = dtable_eval.weightedAreaUnderROC();
            
        Evaluation dtable_eval_validation = new Evaluation(learn);
        Classifier dtable_valid = Classifier.makeCopy(dtable_class);
        dtable_valid.buildClassifier(learn);
        dtable_eval_validation.evaluateModel(dtable_valid, hold);
        validation_ACC[1] = dtable_eval_validation.pctCorrect();
        validation_ROC[1] = dtable_eval_validation.weightedAreaUnderROC();
        
        }
        else if (learn.classAttribute().isNumeric()) // run a linear regression

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            insample_ROC[0] = eval.weightedAreaUnderROC();
        
        Evaluation eval_validation = new Evaluation(learn);
        Classifier valid_loggy = Classifier.makeCopy(linReg);
        valid_loggy.buildClassifier(learn);
        eval_validation.evaluateModel(valid_loggy, hold);
            validation_ACC[0] = eval.pctCorrect();
            validation_ROC[0] = eval.weightedAreaUnderROC();        
        }
        }
        catch (Exception ex){System.out.println(ex.toString());}

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      // Build the classifier
      filteredClassifier = clsCopy;
      filteredClassifier.buildClassifier(train);
      
      Evaluation eval = new Evaluation(train);
          eval.evaluateModel(filteredClassifier, test);
          
          System.out.println(eval.toSummaryString());
        System.out.println(eval.toMatrixString());
    }
    catch (Exception e) {

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