Example usage for weka.classifiers Evaluation toClassDetailsString

List of usage examples for weka.classifiers Evaluation toClassDetailsString

  1. HOME
  2. Java
  3. weka
  4. weka.classifiers.*
  5. Evaluation
  6. toClassDetailsString

Introduction

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

Prototype

public String toClassDetailsString() throws Exception

Source Link

Document

Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.

Usage

From source file:de.tudarmstadt.ukp.alignment.framework.combined.WekaMachineLearning.java

License:Apache License

/**
 *
 * This method creates a serialized WEKA model file from an .arff file containing the annotated gold standard
 *
 *
 * @param gs_arff the annotated gold standard in an .arff file
 * @param model output file for the model
 * @param output_eval if true, the evaluation of the trained classifier is printed (10-fold cross validation)
 * @throws Exception//from w w w. j  a va2 s .  c o  m
 */

public static void createModelFromGoldstandard(String gs_arff, String model, boolean output_eval)
        throws Exception {
    DataSource source = new DataSource(gs_arff);
    Instances data = source.getDataSet();
    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }

    Remove rm = new Remove();
    rm.setAttributeIndices("1"); // remove ID  attribute

    BayesNet bn = new BayesNet(); //Standard classifier; BNs proved most robust, but of course other classifiers are possible
    // meta-classifier
    FilteredClassifier fc = new FilteredClassifier();
    fc.setFilter(rm);
    fc.setClassifier(bn);
    fc.buildClassifier(data); // build classifier
    SerializationHelper.write(model, fc);
    if (output_eval) {
        Evaluation eval = new Evaluation(data);
        eval.crossValidateModel(fc, data, 10, new Random(1));
        System.out.println(eval.toSummaryString());
        System.out.println(eval.toMatrixString());
        System.out.println(eval.toClassDetailsString());
    }

}

From source file:edu.teco.context.recognition.WekaManager.java

License:Apache License

public void testClassification() {
    // set class attribute (last attribute)
    testingData.setClassIndex(testingData.numAttributes() - 1);

    if (FrameworkContext.INFO)
        Log.i("WekaData", "Testing data:\n" + testingData.toString());

    // Test the model
    Evaluation eTest;
    try {/*from   w w w .  java  2 s . com*/
        eTest = new Evaluation(trainingData);
        eTest.evaluateModel(classifier, testingData);

        if (FrameworkContext.INFO)
            Log.i("WekaData", "\nClass detail:\n\n" + eTest.toClassDetailsString());

        // Print the result  la Weka explorer:
        String strSummary = eTest.toSummaryString();
        if (FrameworkContext.INFO)
            Log.i("WekaData", "----- Summary -----\n" + strSummary);

        // print the confusion matrix
        if (FrameworkContext.INFO)
            Log.i("WekaData", "----- Confusion Matrix -----\n" + eTest.toMatrixString());

        // print class details
        if (FrameworkContext.INFO)
            Log.i("WekaData", "----- Class Detail -----\n" + eTest.toClassDetailsString());

        notifyTestCalculated(strSummary);

    } catch (Exception e) {
        e.printStackTrace();
    }

}

From source file:elh.eus.absa.WekaWrapper.java

License:Open Source License

/**
 *  Prints the results stored in an Evaluation object to standard output
 *  (summary, class results and confusion matrix)
 * //  www  . j a  va 2s .c  om
 * @param Evaluation eval
 * @throws Exception
 */
public void printClassifierResults(Evaluation eval) throws Exception {
    // Print the result  la Weka explorer:
    String strSummary = eval.toSummaryString();
    System.out.println(strSummary);

    // Print per class results
    String resPerClass = eval.toClassDetailsString();
    System.out.println(resPerClass);

    // Get the confusion matrix
    String cMatrix = eval.toMatrixString();
    System.out.println(cMatrix);

    System.out.println();
}

From source file:farm_ads.MyClassifier.java

public String printEvaluation(Evaluation e) throws Exception {
    String s = new String();
    s += e.toSummaryString("\nResults\n======\n", false);
    s += "\n" + e.toMatrixString("Matrix String");
    s += "\n" + e.toClassDetailsString();
    return s;/*from  ww w  . j a v  a  2s  . c o m*/
}

From source file:ffnn.FFNN.java

/**
 * @param args the command line arguments
 *///from   w w w .j  av a2s  . c om
public static void main(String[] args) throws Exception {
    FFNNTubesAI cls;
    Scanner scan = new Scanner(System.in);
    System.out.print("new / read? (n/r)");
    if (scan.next().equals("n")) {
        cls = new FFNNTubesAI();
    } else {
        cls = (FFNNTubesAI) TucilWeka.readModel();
    }
    int temp;
    Instances data = TucilWeka.readDataSet("C:\\Program Files\\Weka-3-8\\data\\Team.arff");
    //Tampilkan opsi
    for (int i = 0; i < data.numAttributes(); i++) {
        System.out.println(i + ". " + data.attribute(i));
    }
    System.out.print("Class Index : ");
    temp = scan.nextInt();
    data.setClassIndex(temp);
    data = preprocess(data);
    System.out.println(data);

    System.out.print("full train? (y/n)");
    if (scan.next().equals("y")) {
        try {
            cls.buildClassifier(data);
        } catch (Exception ex) {
            Logger.getLogger(FFNNTubesAI.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
    int fold = 10;

    //FFNNTubesAI.printMatrix(cls.weight1, cls.input_layer+1, cls.hidden_layer);
    //FFNNTubesAI.printMatrix(cls.weight2, cls.hidden_layer, cls.output_layer);
    //FFNNTubesAI.printMatrix(cls.bias2, 1, cls.output_layer);
    Evaluation eval = new Evaluation(data);
    System.out.print("eval/10-fold? (e/f)");
    if (scan.next().equals("e")) {
        eval.evaluateModel(cls, data);
    } else {
        eval.crossValidateModel(cls, data, fold, new Random(1));
    }
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
    System.out.println(eval.toClassDetailsString());
}

From source file:gr.uoc.nlp.opinion.analysis.suggestion.AnalyzeSuggestions.java

/**
 *
 * @param classifier//  w  w  w.  j  a v  a 2s. com
 */
public void crossValidationTrainSet(Classifier classifier) {

    Evaluation eval;
    try {
        //initialize cross validation
        eval = new Evaluation(this.trainset);
        //validate
        eval.crossValidateModel(classifier, this.trainset, 10, new Random(1));

        System.out.println(eval.toSummaryString());
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());

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

From source file:gr.uoc.nlp.opinion.analysis.suggestion.AnalyzeSuggestions.java

/**
 *
 * @param classifier/* w  w w. j  a v a  2 s .c o m*/
 * @param testset
 */
public void valuateSet(Classifier classifier, Instances testset) {

    Evaluation eval;
    try {
        eval = new Evaluation(this.trainset);
        eval.evaluateModel(classifier, testset);

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

From source file:hero.unstable.util.classification.wekaClassifier.java

public Evaluation classify(Instances data) throws Exception {
    data.setClassIndex(0);/*w  w w  .ja va 2s  . c o m*/

    // Randomize data
    Evaluation eval = new Evaluation(data);
    Random rand = new Random(seed);

    // Perform cross-validation
    eval.crossValidateModel(classifier, data, folds, rand);

    // output evaluation
    String result = eval.toClassDetailsString();
    /*
    System.out.println();
    System.out.println("=== Setup ===");
    System.out.println("Clasiffier: " + classifier.toString());
    System.out.println("Dataset: " + data.relationName());
    System.out.println("Folds: " + folds);
    System.out.println("Seed: " + seed);
    System.out.println();
    System.out.println(eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false));
    */
    //System.out.println(result);
    return eval;
}

From source file:machinelearningproject.MachineLearningProject.java

/**
 * @param args the command line arguments
 *//*from  w  w w  .j  a  va  2s .  c o  m*/
public static void main(String[] args) throws Exception {
    // TODO code application logic here
    DataSource source = new DataSource("D:\\spambase.arff");
    //        DataSource source = new DataSource("D:\\weather-nominal.arff");
    Instances instances = source.getDataSet();
    int numAttr = instances.numAttributes();
    instances.setClassIndex(instances.numAttributes() - 1);

    int runs = 5;
    int seed = 15;
    for (int i = 0; i < runs; i++) {
        //randomize data
        seed = seed + 1; // the seed for randomizing the data
        Random rand = new Random(seed); // create seeded number generator
        Instances randData = new Instances(instances); // create copy of original data
        Collections.shuffle(randData);

        Evaluation evalDTree = new Evaluation(randData);
        Evaluation evalRF = new Evaluation(randData);
        Evaluation evalSVM = new Evaluation(randData);

        int folds = 10;
        for (int n = 0; n < folds; n++) {
            Instances train = randData.trainCV(folds, n, rand);
            Instances test = randData.testCV(folds, n);
            //instantiate classifiers
            DecisionTree dtree = new DecisionTree();
            RandomForest rf = new RandomForest(100);
            SMO svm = new SMO();
            RBFKernel rbfKernel = new RBFKernel();
            double gamma = 0.70;
            rbfKernel.setGamma(gamma);

            dtree.buildClassifier(train);
            rf.buildClassifier(train);
            svm.buildClassifier(train);

            evalDTree.evaluateModel(dtree, test);
            evalRF.evaluateModel(rf, test);
            evalSVM.evaluateModel(svm, test);
        }
        System.out.println("=== Decision Tree Evaluation ===");
        System.out.println(evalDTree.toSummaryString());
        System.out.println(evalDTree.toClassDetailsString());
        System.out.println(evalDTree.toMatrixString());

        System.out.println("=== Random Forest Evaluation ===");
        System.out.println(evalRF.toSummaryString());
        System.out.println(evalRF.toClassDetailsString());
        System.out.println(evalRF.toMatrixString());

        System.out.println("=== SVM Evaluation ===");
        System.out.println(evalSVM.toSummaryString());
        System.out.println(evalSVM.toClassDetailsString());
        System.out.println(evalSVM.toMatrixString());
    }
}

From source file:miRdup.WekaModule.java

License:Open Source License

public static void trainModel(File arff, String keyword) {
    dec.setMaximumFractionDigits(3);// ww w .  j  av  a  2 s .com
    System.out.println("\nTraining model on file " + arff);
    try {
        // load data
        DataSource source = new DataSource(arff.toString());
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        PrintWriter pwout = new PrintWriter(new FileWriter(keyword + Main.modelExtension + "Output"));
        PrintWriter pwroc = new PrintWriter(new FileWriter(keyword + Main.modelExtension + "roc.arff"));

        //remove ID row
        Remove rm = new Remove();
        rm.setAttributeIndices("1");
        FilteredClassifier fc = new FilteredClassifier();
        fc.setFilter(rm);

        //            // train model svm
        //            weka.classifiers.functions.LibSVM model = new weka.classifiers.functions.LibSVM();
        //            model.setOptions(weka.core.Utils.splitOptions("-S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.0010 -P 0.1 -B"));
        // train model MultilayerPerceptron
        //            weka.classifiers.functions.MultilayerPerceptron model = new weka.classifiers.functions.MultilayerPerceptron();
        //            model.setOptions(weka.core.Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"));
        // train model Adaboost on RIPPER
        //            weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //            model.setOptions(weka.core.Utils.splitOptions("weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.rules.JRip -- -F 10 -N 2.0 -O 5 -S 1"));
        // train model Adaboost on FURIA
        //            weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //            model.setOptions(weka.core.Utils.splitOptions("weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.rules.FURIA -- -F 10 -N 2.0 -O 5 -S 1 -p 0 -s 0"));
        //train model Adaboot on J48 trees
        //             weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        //             model.setOptions(
        //                     weka.core.Utils.splitOptions(
        //                     "-P 100 -S 1 -I 10 -W weka.classifiers.trees.J48 -- -C 0.25 -M 2"));
        //train model Adaboot on Random Forest trees
        weka.classifiers.meta.AdaBoostM1 model = new weka.classifiers.meta.AdaBoostM1();
        model.setOptions(weka.core.Utils
                .splitOptions("-P 100 -S 1 -I 10 -W weka.classifiers.trees.RandomForest -- -I 50 -K 0 -S 1"));

        if (Main.debug) {
            System.out.print("Model options: " + model.getClass().getName().trim() + " ");
        }
        System.out.print(model.getClass() + " ");
        for (String s : model.getOptions()) {
            System.out.print(s + " ");
        }

        pwout.print("Model options: " + model.getClass().getName().trim() + " ");
        for (String s : model.getOptions()) {
            pwout.print(s + " ");
        }

        //build model
        //            model.buildClassifier(data);
        fc.setClassifier(model);
        fc.buildClassifier(data);

        // cross validation 10 times on the model
        Evaluation eval = new Evaluation(data);
        //eval.crossValidateModel(model, data, 10, new Random(1));
        StringBuffer sb = new StringBuffer();
        eval.crossValidateModel(fc, data, 10, new Random(1), sb, new Range("first,last"), false);

        //System.out.println(sb);
        pwout.println(sb);
        pwout.flush();

        // output
        pwout.println("\n" + eval.toSummaryString());
        System.out.println(eval.toSummaryString());

        pwout.println(eval.toClassDetailsString());
        System.out.println(eval.toClassDetailsString());

        //calculate importants values
        String ev[] = eval.toClassDetailsString().split("\n");

        String ptmp[] = ev[3].trim().split(" ");
        String ntmp[] = ev[4].trim().split(" ");
        String avgtmp[] = ev[5].trim().split(" ");

        ArrayList<String> p = new ArrayList<String>();
        ArrayList<String> n = new ArrayList<String>();
        ArrayList<String> avg = new ArrayList<String>();

        for (String s : ptmp) {
            if (!s.trim().isEmpty()) {
                p.add(s);
            }
        }
        for (String s : ntmp) {
            if (!s.trim().isEmpty()) {
                n.add(s);
            }
        }
        for (String s : avgtmp) {
            if (!s.trim().isEmpty()) {
                avg.add(s);
            }
        }

        double tp = Double.parseDouble(p.get(0));
        double fp = Double.parseDouble(p.get(1));
        double tn = Double.parseDouble(n.get(0));
        double fn = Double.parseDouble(n.get(1));
        double auc = Double.parseDouble(avg.get(7));

        pwout.println("\nTP=" + tp + "\nFP=" + fp + "\nTN=" + tn + "\nFN=" + fn);
        System.out.println("\nTP=" + tp + "\nFP=" + fp + "\nTN=" + tn + "\nFN=" + fn);

        //specificity, sensitivity, Mathew's correlation, Prediction accuracy
        double sp = ((tn) / (tn + fp));
        double se = ((tp) / (tp + fn));
        double acc = ((tp + tn) / (tp + tn + fp + fn));
        double mcc = ((tp * tn) - (fp * fn)) / Math.sqrt((tp + fp) * (tn + fn) * (tp + fn) * tn + fp);

        String output = "\nse=" + dec.format(se).replace(",", ".") + "\nsp=" + dec.format(sp).replace(",", ".")
                + "\nACC=" + dec.format(acc).replace(",", ".") + "\nMCC=" + dec.format(mcc).replace(",", ".")
                + "\nAUC=" + dec.format(auc).replace(",", ".");

        pwout.println(output);
        System.out.println(output);

        pwout.println(eval.toMatrixString());
        System.out.println(eval.toMatrixString());

        pwout.flush();
        pwout.close();

        //Saving model
        System.out.println("Model saved: " + keyword + Main.modelExtension);
        weka.core.SerializationHelper.write(keyword + Main.modelExtension, fc.getClassifier() /*model*/);

        // get curve
        ThresholdCurve tc = new ThresholdCurve();
        int classIndex = 0;
        Instances result = tc.getCurve(eval.predictions(), classIndex);
        pwroc.print(result.toString());
        pwroc.flush();
        pwroc.close();

        // draw curve
        //rocCurve(eval);
    } catch (Exception e) {
        e.printStackTrace();
    }
}