Example usage for weka.classifiers Evaluation Evaluation

List of usage examples for weka.classifiers Evaluation Evaluation

Introduction

In this page you can find the example usage for weka.classifiers Evaluation Evaluation.

Prototype

public Evaluation(Instances data) throws Exception 

Source Link

Usage

From source file:DocClassifier.java

public Evaluation classify(Classifier classifier) throws Exception {
    docPredList.clear();//from w  w  w  .  j a  v  a 2  s.co m
    Instances testInstances = createInstances(testFiles);
    Instances trainInstances = createInstances(trainFiles);
    classifier.buildClassifier(trainInstances);
    Evaluation ev = new Evaluation(trainInstances);
    for (int i = 0; i < testInstances.numInstances(); ++i) {
        Instance inst = testInstances.instance(i);
        double pred = ev.evaluateModelOnceAndRecordPrediction(classifier, inst);
        docPredList.add(testFiles[i].getName() + "\t=>\t" + inst.classAttribute().value((int) pred));
    }
    return ev;
}

From source file:DocClassifier.java

public Evaluation cvClassify(Classifier classifier, int k) throws Exception {
    docPredList.clear();/*from   ww  w .  j a  va  2 s  .  co  m*/
    Instances trainInstances = createInstances(trainFiles);
    Evaluation ev = new Evaluation(trainInstances);
    ev.crossValidateModel(classifier, trainInstances, k, new Random(1));
    return ev;
}

From source file:FlexDMThread.java

License:Open Source License

public void run() {
    try {//from   w  w  w . jav a2  s .  c  om
        //Get the data from the source

        FlexDM.getMainData.acquire();
        Instances data = dataset.getSource().getDataSet();
        FlexDM.getMainData.release();

        //Set class attribute if undefined
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        //Process hyperparameters for classifier
        String temp = "";
        for (int i = 0; i < classifier.getNumParams(); i++) {
            temp += classifier.getParameter(i).getName();
            temp += " ";
            if (classifier.getParameter(i).getValue() != null) {
                temp += classifier.getParameter(i).getValue();
                temp += " ";
            }
        }

        String[] options = weka.core.Utils.splitOptions(temp);

        //Print to console- experiment is starting
        if (temp.equals("")) { //no parameters
            temp = "results_no_parameters";
            try {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with no parameters");
            } catch (Exception e) {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with no parameters");
            }
        } else { //parameters
            try {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with parameters " + temp);
            } catch (Exception e) {
                System.out.println("STARTING CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with parameters " + temp);
            }
        }

        //Create classifier, setting parameters
        weka.classifiers.Classifier x = createObject(classifier.getName());
        x.setOptions(options);
        x.buildClassifier(data);

        //Process the test selection
        String[] tempTest = dataset.getTest().split("\\s");

        //Create evaluation object for training and testing classifiers
        Evaluation eval = new Evaluation(data);
        StringBuffer predictions = new StringBuffer();

        //Train and evaluate classifier
        if (tempTest[0].equals("testset")) { //specified test file
            //Build classifier
            x.buildClassifier(data);

            //Open test file, load data
            //DataSource testFile = new DataSource(dataset.getTest().substring(7).trim());
            // Instances testSet = testFile.getDataSet();
            FlexDM.getTestData.acquire();
            Instances testSet = dataset.getTestFile().getDataSet();
            FlexDM.getTestData.release();

            //Set class attribute if undefined
            if (testSet.classIndex() == -1) {
                testSet.setClassIndex(testSet.numAttributes() - 1);
            }

            //Evaluate model
            Object[] array = { predictions, new Range(), new Boolean(true) };
            eval.evaluateModel(x, testSet, array);
        } else if (tempTest[0].equals("xval")) { //Cross validation
            //Build classifier
            x.buildClassifier(data);

            //Cross validate
            eval.crossValidateModel(x, data, Integer.parseInt(tempTest[1]), new Random(1), predictions,
                    new Range(), true);
        } else if (tempTest[0].equals("leavexval")) { //Leave one out cross validation
            //Build classifier
            x.buildClassifier(data);

            //Cross validate
            eval.crossValidateModel(x, data, data.numInstances() - 1, new Random(1), predictions, new Range(),
                    true);
        } else if (tempTest[0].equals("percent")) { //Percentage split of single data set
            //Set training and test sizes from percentage
            int trainSize = (int) Math.round(data.numInstances() * Double.parseDouble(tempTest[1]));
            int testSize = data.numInstances() - trainSize;

            //Load specified data
            Instances train = new Instances(data, 0, trainSize);
            Instances testSet = new Instances(data, trainSize, testSize);

            //Build classifier
            x.buildClassifier(train);

            //Train and evaluate model
            Object[] array = { predictions, new Range(), new Boolean(true) };
            eval.evaluateModel(x, testSet, array);
        } else { //Evaluate on training data
            //Test and evaluate model
            Object[] array = { predictions, new Range(), new Boolean(true) };
            eval.evaluateModel(x, data, array);
        }

        //create datafile for results
        String filename = dataset.getDir() + "/" + classifier.getDirName() + "/" + temp + ".txt";
        PrintWriter writer = new PrintWriter(filename, "UTF-8");

        //Print classifier, dataset, parameters info to file
        try {
            writer.println("CLASSIFIER: " + classifier.getName() + "\n DATASET: " + dataset.getName()
                    + "\n PARAMETERS: " + temp);
        } catch (Exception e) {
            writer.println("CLASSIFIER: " + classifier.getName() + "\n DATASET: " + dataset.getName()
                    + "\n PARAMETERS: " + temp);
        }

        //Add evaluation string to file
        writer.println(eval.toSummaryString());
        //Process result options
        if (checkResults("stats")) { //Classifier statistics
            writer.println(eval.toClassDetailsString());
        }
        if (checkResults("model")) { //The model
            writer.println(x.toString());
        }
        if (checkResults("matrix")) { //Confusion matrix
            writer.println(eval.toMatrixString());
        }
        if (checkResults("entropy")) { //Entropy statistics
            //Set options req'd to get the entropy stats
            String[] opt = new String[4];
            opt[0] = "-t";
            opt[1] = dataset.getName();
            opt[2] = "-k";
            opt[3] = "-v";

            //Evaluate model
            String entropy = Evaluation.evaluateModel(x, opt);

            //Grab the relevant info from the results, print to file
            entropy = entropy.substring(entropy.indexOf("=== Stratified cross-validation ===") + 35,
                    entropy.indexOf("=== Confusion Matrix ==="));
            writer.println("=== Entropy Statistics ===");
            writer.println(entropy);
        }
        if (checkResults("predictions")) { //The models predictions
            writer.println("=== Predictions ===\n");
            if (!dataset.getTest().contains("xval")) { //print header of predictions table if req'd
                writer.println(" inst#     actual  predicted error distribution ()");
            }
            writer.println(predictions.toString()); //print predictions to file
        }

        writer.close();

        //Summary file is semaphore controlled to ensure quality
        try { //get a permit
              //grab the summary file, write the classifiers details to it
            FlexDM.writeFile.acquire();
            PrintWriter p = new PrintWriter(new FileWriter(summary, true));
            if (temp.equals("results_no_parameters")) { //change output based on parameters
                temp = temp.substring(8);
            }

            //write percent correct, classifier name, dataset name to summary file
            p.write(dataset.getName() + ", " + classifier.getName() + ", " + temp + ", " + eval.correct() + ", "
                    + eval.incorrect() + ", " + eval.unclassified() + ", " + eval.pctCorrect() + ", "
                    + eval.pctIncorrect() + ", " + eval.pctUnclassified() + ", " + eval.kappa() + ", "
                    + eval.meanAbsoluteError() + ", " + eval.rootMeanSquaredError() + ", "
                    + eval.relativeAbsoluteError() + ", " + eval.rootRelativeSquaredError() + ", "
                    + eval.SFPriorEntropy() + ", " + eval.SFSchemeEntropy() + ", " + eval.SFEntropyGain() + ", "
                    + eval.SFMeanPriorEntropy() + ", " + eval.SFMeanSchemeEntropy() + ", "
                    + eval.SFMeanEntropyGain() + ", " + eval.KBInformation() + ", " + eval.KBMeanInformation()
                    + ", " + eval.KBRelativeInformation() + ", " + eval.weightedTruePositiveRate() + ", "
                    + eval.weightedFalsePositiveRate() + ", " + eval.weightedTrueNegativeRate() + ", "
                    + eval.weightedFalseNegativeRate() + ", " + eval.weightedPrecision() + ", "
                    + eval.weightedRecall() + ", " + eval.weightedFMeasure() + ", "
                    + eval.weightedAreaUnderROC() + "\n");
            p.close();

            //release semaphore
            FlexDM.writeFile.release();
        } catch (InterruptedException e) { //bad things happened
            System.err.println("FATAL ERROR OCCURRED: Classifier: " + cNum + " - " + classifier.getName()
                    + " on dataset " + dataset.getName());
        }

        //output we have successfully finished processing classifier
        if (temp.equals("no_parameters")) { //no parameters
            try {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with no parameters");
            } catch (Exception e) {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with no parameters");
            }
        } else { //with parameters
            try {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName().substring(dataset.getName().lastIndexOf("\\") + 1)
                        + " with parameters " + temp);
            } catch (Exception e) {
                System.out.println("FINISHED CLASSIFIER " + cNum + " - " + classifier.getName() + " on dataset "
                        + dataset.getName() + " with parameters " + temp);
            }
        }

        try { //get a permit
              //grab the log file, write the classifiers details to it
            FlexDM.writeLog.acquire();
            PrintWriter p = new PrintWriter(new FileWriter(log, true));

            Date date = new Date();
            Format formatter = new SimpleDateFormat("dd/MM/YYYY HH:mm:ss");
            //formatter.format(date)

            if (temp.equals("results_no_parameters")) { //change output based on parameters
                temp = temp.substring(8);
            }

            //write details to log file
            p.write(dataset.getName() + ", " + dataset.getTest() + ", \"" + dataset.getResult_string() + "\", "
                    + classifier.getName() + ", " + temp + ", " + formatter.format(date) + "\n");
            p.close();

            //release semaphore
            FlexDM.writeLog.release();
        } catch (InterruptedException e) { //bad things happened
            System.err.println("FATAL ERROR OCCURRED: Classifier: " + cNum + " - " + classifier.getName()
                    + " on dataset " + dataset.getName());
        }

        s.release();

    } catch (Exception e) {
        //an error occurred
        System.err.println("FATAL ERROR OCCURRED: " + e.toString() + "\nClassifier: " + cNum + " - "
                + classifier.getName() + " on dataset " + dataset.getName());
        s.release();
    }

}

From source file:CrossValidationMultipleRuns.java

License:Open Source License

/**
 * Performs the cross-validation. See Javadoc of class for information
 * on command-line parameters./*  www .  ja  v a  2s.  c o  m*/
 *
 * @param args   the command-line parameters
 * @throws Exception   if something goes wrong
 */
public static void main(String[] args) throws Exception {
    // loads data and set class index
    Instances data = DataSource.read(Utils.getOption("t", args));
    String clsIndex = Utils.getOption("c", args);
    if (clsIndex.length() == 0)
        clsIndex = "last";
    if (clsIndex.equals("first"))
        data.setClassIndex(0);
    else if (clsIndex.equals("last"))
        data.setClassIndex(data.numAttributes() - 1);
    else
        data.setClassIndex(Integer.parseInt(clsIndex) - 1);

    // classifier
    String[] tmpOptions;
    String classname;
    tmpOptions = Utils.splitOptions(Utils.getOption("W", args));
    classname = tmpOptions[0];
    tmpOptions[0] = "";
    Classifier cls = (Classifier) Utils.forName(Classifier.class, classname, tmpOptions);

    // other options
    int runs = Integer.parseInt(Utils.getOption("r", args));
    int folds = Integer.parseInt(Utils.getOption("x", args));

    // perform cross-validation
    for (int i = 0; i < runs; i++) {
        // randomize data
        int seed = i + 1;
        Random rand = new Random(seed);
        Instances randData = new Instances(data);
        randData.randomize(rand);
        //if (randData.classAttribute().isNominal())
        //   randData.stratify(folds);

        Evaluation eval = new Evaluation(randData);

        StringBuilder optionsString = new StringBuilder();
        for (String s : cls.getOptions()) {
            optionsString.append(s);
            optionsString.append(" ");
        }

        // output evaluation
        System.out.println();
        System.out.println("=== Setup run " + (i + 1) + " ===");
        System.out.println("Classifier: " + optionsString.toString());
        System.out.println("Dataset: " + data.relationName());
        System.out.println("Folds: " + folds);
        System.out.println("Seed: " + seed);
        System.out.println();

        for (int n = 0; n < folds; n++) {
            Instances train = randData.trainCV(folds, n);
            Instances test = randData.testCV(folds, n);

            // build and evaluate classifier
            Classifier clsCopy = Classifier.makeCopy(cls);
            clsCopy.buildClassifier(train);
            eval.evaluateModel(clsCopy, test);
            System.out.println(eval.toClassDetailsString());
        }

        System.out.println(
                eval.toSummaryString("=== " + folds + "-fold Cross-validation run " + (i + 1) + " ===", false));
    }
}

From source file:homemadeWEKA.java

public static void treeLearning_crossVal(Instances data) throws Exception {
    Evaluation eval = new Evaluation(data);
    J48 tree = new J48();
    eval.crossValidateModel(tree, data, 10, new Random(1));
    save_modelWithEval(tree, eval);/*from w ww .j a va2 s  . c om*/
    //        System.out.println(eval.toSummaryString("\nResult of tree learning with cross validation 10 folds\n \n",false));
}

From source file:homemadeWEKA.java

public static void reevaluateModel(Instances data_train, Instances data_test, Classifier cls) throws Exception {
    Evaluation eval = new Evaluation(data_train);
    eval.evaluateModel(cls, data_test);/*from   w  w w .j  a  v  a  2s .co m*/
    System.out.println(eval.toSummaryString("\nResults\n\n", false));
}

From source file:ClassificationClass.java

public Evaluation cls_svm(Instances data) {
    Evaluation eval = null;//  ww  w.j  a v  a2  s . co m
    try {
        Classifier classifier;
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new SMO();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:ClassificationClass.java

public Evaluation cls_knn(Instances data) {
    Evaluation eval = null;//from  w  w  w.ja  va2 s. c  o m
    try {
        Classifier classifier;
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new IBk();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

        System.out.println(eval.weightedFMeasure());
    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:ClassificationClass.java

public Evaluation cls_naivebayes(Instances data) {
    Evaluation eval = null;//from   w  w  w .  j  a v a  2s .c om
    try {
        Classifier classifier;
        PreparingSteps preparingSteps = new PreparingSteps();
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new NaiveBayes();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

        System.out.println(eval.toSummaryString());
    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}

From source file:ClassificationClass.java

public Evaluation cls_c4_5(Instances data) {
    Evaluation eval = null;/*  www .  j  a v a  2s. c o m*/
    try {
        Classifier classifier;
        PreparingSteps preparingSteps = new PreparingSteps();
        data.setClassIndex(data.numAttributes() - 1);
        classifier = new J48();
        classifier.buildClassifier(data);
        eval = new Evaluation(data);
        eval.evaluateModel(classifier, data);

        System.out.println(eval.toSummaryString());
    } catch (Exception ex) {
        Logger.getLogger(ClassificationClass.class.getName()).log(Level.SEVERE, null, ex);
    }
    return eval;
}