Example usage for weka.classifiers Evaluation toSummaryString

Introduction

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

Prototype

public String toSummaryString(String title, boolean printComplexityStatistics) 

Source Link

Document

Outputs the performance statistics in summary form.

Usage

From source file:com.guidefreitas.locator.main.Main.java

public static void main(String[] args) throws Exception {
    //String dataFile = "wifi_data.txt";
    String dataFile = "wifi_data_university.txt";

    DatabaseManager.createDatabase();/*  w  w w. j  a v a  2  s  . c om*/
    DatabaseManager.seedLocations(dataFile);
    PredictionService predService = PredictionService.getInstance();
    predService.generateTrainData();
    Evaluation eval = predService.train();
    System.out.println("================================================");
    System.out.println(eval.toSummaryString("\nResults\n======\n", true));
    System.out.println("================================================");
    System.out.println(eval.toMatrixString("\nConfusion Matrix\n======\n"));
    System.out.println("================================================");
}

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From source file:com.mycompany.id3classifier.ID3Shell.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("lensesData.csv");
    Instances dataSet = source.getDataSet();

    Discretize filter = new Discretize();
    filter.setInputFormat(dataSet);/*w  ww . j  av a2  s.c o  m*/
    dataSet = Filter.useFilter(dataSet, filter);

    Standardize standardize = new Standardize();
    standardize.setInputFormat(dataSet);
    dataSet = Filter.useFilter(dataSet, standardize);

    dataSet.setClassIndex(dataSet.numAttributes() - 1);
    dataSet.randomize(new Random(9001)); //It's over 9000!!

    int folds = 10;
    //Perform crossvalidation
    Evaluation eval = new Evaluation(dataSet);
    for (int n = 0; n < folds; n++) {
        int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
        int testSize = dataSet.numInstances() - trainingSize;

        Instances trainingData = dataSet.trainCV(folds, n);
        Instances testData = dataSet.testCV(folds, n);

        ID3Classifier classifier = new ID3Classifier();
        // Id3 classifier = new Id3();
        classifier.buildClassifier(trainingData);

        eval.evaluateModel(classifier, testData);
    }
    System.out.println(eval.toSummaryString("\nResults:\n", false));
}

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From source file:com.mycompany.knnclassifier.kNNShell.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("carData.csv");
    Instances dataSet = source.getDataSet();

    Standardize standardize = new Standardize();
    standardize.setInputFormat(dataSet);
    dataSet = Filter.useFilter(dataSet, standardize);

    dataSet.setClassIndex(dataSet.numAttributes() - 1);
    dataSet.randomize(new Random(9001)); //It's over 9000!!

    int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
    int testSize = dataSet.numInstances() - trainingSize;

    Instances trainingData = new Instances(dataSet, 0, trainingSize);
    Instances testData = new Instances(dataSet, trainingSize, testSize);

    kNNClassifier classifier = new kNNClassifier(3);
    classifier.buildClassifier(trainingData);

    //Used to compare to Weka's built in KNN algorithm
    //Classifier classifier = new IBk(1);
    //classifier.buildClassifier(trainingData);

    Evaluation eval = new Evaluation(trainingData);
    eval.evaluateModel(classifier, testData);

    System.out.println(eval.toSummaryString("\nResults:\n", false));
}

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From source file:com.mycompany.neuralnetwork.NeuralNetworkShell.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource("irisData.csv");
    Instances dataSet = source.getDataSet();

    Standardize standardize = new Standardize();
    standardize.setInputFormat(dataSet);
    dataSet = Filter.useFilter(dataSet, standardize);
    dataSet.setClassIndex(dataSet.numAttributes() - 1);
    dataSet.randomize(new Random(9001)); //It's over 9000!!

    int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
    int testSize = dataSet.numInstances() - trainingSize;

    Instances trainingData = new Instances(dataSet, 0, trainingSize);
    Instances testData = new Instances(dataSet, trainingSize, testSize);

    //MultilayerPerceptron classifier = new MultilayerPerceptron();
    NeuralNetworkClassifier classifier = new NeuralNetworkClassifier(3, 20000, 0.1);
    classifier.buildClassifier(trainingData);

    Evaluation eval = new Evaluation(trainingData);
    eval.evaluateModel(classifier, testData);

    System.out.println(eval.toSummaryString("\nResults:\n", false));
}

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From source file:core.classification.Classifiers.java

License:Open Source License

public void trainSC() throws Exception {
    String sql;//  ww w.j a va2 s.  c om

    // ---
    // Connect to the database
    // ---
    InstanceQuery query = new InstanceQuery();
    query.setDatabaseURL(dbase);
    query.setUsername("");
    query.setPassword("");

    // ---
    // ---
    // SCA
    // ---
    // ---

    sql = "SELECT ";
    sql += "CR.ratio, CR.class ";
    sql += "FROM Class_ratio AS CR;";

    query.setQuery(sql);
    Instances data = query.retrieveInstances();

    // ---
    // Setting options
    // ---
    String[] options = Utils.splitOptions(
            "-D -Q weka.classifiers.bayes.net.search.local.K2 -- -P 1 -S BAYES -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5");
    SCA.setOptions(options);
    data.setClassIndex(data.numAttributes() - 1);

    // ---
    // Train the classifier
    // ---
    System.out.println("Building SCA ...");
    SCA.buildClassifier(data);
    System.out.println("Done.");

    // ---
    // Classifier evaluation
    // ---
    System.out.println("Cross-validation for SCA...");
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(SCA, data, 10, new Random(1));
    System.out.println("Done.");
    System.out.println(eval.toSummaryString("\n Results for SCA: \n\n", false));

    // ---
    // ---
    // SCB
    // ---
    // ---

    sql = "SELECT ";
    sql += "Data.H2, Data.D2, Data.DX, ";
    sql += "Data.PARENT_CHAR AS PCLASS, ";
    sql += "Data.CLASS ";
    sql += "FROM Data ";
    sql += "WHERE (((Data.SEGERR)=0) AND (Data.PARENT_CHAR<>'0') );";

    query.setQuery(sql);
    data = query.retrieveInstances();

    // ---
    // Setting options
    // ---
    options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a");
    SCB.setOptions(options);
    data.setClassIndex(data.numAttributes() - 1);

    // ---
    // Train the classifier
    // ---
    System.out.println("Building SCB ...");
    SCB.buildClassifier(data);
    System.out.println("Done.");

    // ---
    // Classifier evaluation
    // ---
    System.out.println("Cross-validation for SCB...");
    eval = new Evaluation(data);
    eval.crossValidateModel(SCB, data, 10, new Random(1));
    System.out.println("Done.");
    System.out.println(eval.toSummaryString("\n Results for SCB: \n\n", false));

    // ---
    // ---
    // SCC
    // ---
    // ---

    // ----
    // SCC1
    // ----

    sql = "SELECT ";
    sql += "Data.LH, Data.LD, Data.LDX, Data.LCLASS, ";
    sql += "Data.CLASS ";
    sql += "FROM Data ";
    sql += "WHERE ( (Data.SEGERR)=0  AND ( (Data.LCLASS)<>'0' ) );";

    query.setQuery(sql);
    data = query.retrieveInstances();

    // ---
    // Setting options
    // ---
    options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a");
    SCC1.setOptions(options);
    data.setClassIndex(data.numAttributes() - 1);

    // ---
    // Train the classifier
    // ---
    System.out.println("Building SCC1 ...");
    SCC1.buildClassifier(data);
    System.out.println("Done.");

    // ---
    // Classifier evaluation
    // ---
    System.out.println("Cross-validation for SCC1...");
    eval = new Evaluation(data);
    eval.crossValidateModel(SCC1, data, 10, new Random(1));
    System.out.println("Done.");
    System.out.println(eval.toSummaryString("\n Results for SCC1: \n\n", false));

    // ----
    // SCC2
    // ----

    sql = "SELECT ";
    sql += "Data.EH, Data.ED, Data.EDX, Data.ECLASS, ";
    sql += "Data.CLASS ";
    sql += "FROM Data ";
    sql += "WHERE ( (Data.SEGERR)=0  AND ( (Data.ECLASS)<>'0' ) );";

    query.setQuery(sql);
    data = query.retrieveInstances();

    // ---
    // Setting options
    // ---
    //      options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a");
    SCC2.setOptions(options);
    data.setClassIndex(data.numAttributes() - 1);

    // ---
    // Train the classifier
    // ---
    System.out.println("Building SCC2 ...");
    SCC2.buildClassifier(data);
    System.out.println("Done.");

    // ---
    // Classifier evaluation
    // ---
    System.out.println("Cross-validation for SCC2...");
    eval = new Evaluation(data);
    eval.crossValidateModel(SCC2, data, 10, new Random(1));
    System.out.println("Done.");
    System.out.println(eval.toSummaryString("\n Results for SCC2: \n\n", false));

    // ----
    // SCC3
    // ----

    sql = "SELECT ";
    sql += "Data.SH, Data.SD, Data.SDX, Data.SCLASS, ";
    sql += "Data.CLASS ";
    sql += "FROM Data ";
    sql += "WHERE ( (Data.SEGERR)=0  AND ( (Data.SCLASS)<>'0' ) );";

    query.setQuery(sql);
    data = query.retrieveInstances();

    // ---
    // Setting options
    // ---
    //      options = Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a");
    SCC3.setOptions(options);
    data.setClassIndex(data.numAttributes() - 1);

    // ---
    // Train the classifier
    // ---
    System.out.println("Building SCC3 ...");
    SCC3.buildClassifier(data);
    System.out.println("Done.");

    // ---
    // Classifier evaluation
    // ---
    System.out.println("Cross-validation for SCC3...");
    eval = new Evaluation(data);
    eval.crossValidateModel(SCC3, data, 10, new Random(1));
    System.out.println("Done.");
    System.out.println(eval.toSummaryString("\n Results for SCC3: \n\n", false));

}

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From source file:core.classification.Classifiers.java

License:Open Source License

public void trainRC() throws Exception {
    // ---/*from  w  w  w . j a  v a2 s .c o m*/
    // Retrieve the instances in the database
    // ---
    InstanceQuery query = new InstanceQuery();
    query.setDatabaseURL(dbase);
    query.setUsername("");
    query.setPassword("");

    String sql = "SELECT ";
    sql += "Data.H2, Data.D2, Data.DX, ";
    sql += "Data.CLASS, Data.PARENT_CHAR AS PCLASS, ";
    sql += "Data.RELID ";
    sql += "FROM Data ";
    sql += "WHERE (((Data.SEGERR)=0) AND (Data.PARENT_CHAR<>'0') );";

    query.setQuery(sql);
    Instances data = query.retrieveInstances();

    // ---
    // Setting options
    // ---
    //      String[] options = Utils.splitOptions("-L 0.2 -M 0.2 -N 50 -V 0 -S 0 -E 20 -H 5 ");
    String[] options = Utils.splitOptions(
            "-cost-matrix \"[0.0 1.0 1.0 0.1 0.1; 1.0 0.0 1.0 0.1 0.1; 1.0 1.0 0.0 0.1 0.1; 10.0 10.0 10.0 0.0 1.0; 10.0 10.0 10.0 1.0 0.0]\" -S 1 -W weka.classifiers.functions.MultilayerPerceptron -- -L 0.2 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a");
    RC.setOptions(options);
    data.setClassIndex(data.numAttributes() - 1);

    // ---
    // Train
    // ---
    System.out.println("Building RC...");
    RC.buildClassifier(data);
    System.out.println("Done.");

    // ---
    // Evaluation
    // ---
    System.out.println("Cross-validation for RC...");
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(RC, data, 10, new Random(1));
    System.out.println("Done.");
    System.out.println(eval.toSummaryString("\n Results for RC: \n\n", false));

}

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From source file:core.classification.Classifiers.java

License:Open Source License

public void trainYNC() throws Exception {
    // ---/*from   w w w. ja  va  2 s  . c  om*/
    // Retrieve the instances in the database
    // ---
    InstanceQuery query = new InstanceQuery();
    query.setDatabaseURL(dbase);
    query.setUsername("");
    query.setPassword("");

    String sql = "SELECT ";
    sql += "YNCdata.PCLASS, YNCdata.CCLASS, YNCdata.RAREA, YNCdata.H, YNCdata.D, YNCdata.V, ";
    sql += "YNCdata.YN ";
    sql += "FROM YNCdata ";

    query.setQuery(sql);
    Instances data = query.retrieveInstances();

    // ---
    // Setting options
    // ---
    String[] options = Utils.splitOptions("-R -N 3 -Q 1 -M 30");
    YNC.setOptions(options);
    data.setClassIndex(data.numAttributes() - 1);

    // ---
    // Train
    // ---
    System.out.println("Building YC...");
    YNC.buildClassifier(data);
    System.out.println("Done.");

    // ---
    // Evaluation
    // ---
    System.out.println("Cross-validation for YNC...");
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(YNC, data, 10, new Random(1));
    System.out.println("Done.");
    System.out.println(eval.toSummaryString("\n Results for YNC: \n\n", false));

}

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From source file:examples.Pair.java

License:Open Source License

/**
 * @param args the command line arguments
 *///from  w  w  w  .  ja  v a2  s .co  m
public static void main(String[] args) throws Exception {

    if (args.length != 1) {
        System.out.println("Requires path to the dataset as the first and only argument");
        return;
    }

    final String datasetPath = args[0];

    // Create classifiers
    MultiStageCascading msc = new MultiStageCascading();
    J48 classifier1 = new J48();
    IBk knn = new IBk(3);

    // Set sequence of classifiers
    msc.setClassifiers(new Classifier[] { classifier1, new NBTree() });
    msc.setDebug(true);
    // Set a classifier that will classify an instance that is not classified by all other classifiers
    msc.setLastClassifier(knn);

    // First classifier will have confidence threshold 0.95 and the second one 0.97
    msc.setConfidenceThresholds("0.95,0.97");
    // 80% of instances in training set will be randomly selected to train j-th classifier
    msc.setPercentTrainingInstances(0.8);

    Instances dataset = DataSource.read(datasetPath);
    dataset.setClassIndex(dataset.numAttributes() - 1);

    // Create test and training sets
    Pair<Instances, Instances> sets = seprateTestAndTrainingSets(dataset, 0.7);
    Instances trainingSet = sets.getFirst();
    Instances testSet = sets.getSecond();

    // Build cascade classifier
    msc.buildClassifier(trainingSet);

    // Evaluate created classifier
    Evaluation eval = new Evaluation(trainingSet);
    eval.evaluateModel(msc, testSet);
    System.out.println(eval.toSummaryString("\nResults\n\n", false));
}

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From source file:experimentalclassifier.ExperimentalClassifier.java

/**
 * @param args the command line arguments
 *//*  w  w w  . j a v  a2s.c  om*/
public static void main(String[] args) throws Exception {

    DataSource source = new DataSource("data/iris.csv");

    Instances data = source.getDataSet();

    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }

    data.randomize(new Random());

    String[] options = weka.core.Utils.splitOptions("-P 30");
    RemovePercentage remove = new RemovePercentage();
    remove.setOptions(options);
    remove.setInputFormat(data);
    Instances train = Filter.useFilter(data, remove);

    remove.setInvertSelection(true);
    remove.setInputFormat(data);
    Instances test = Filter.useFilter(data, remove);

    Classifier classifier = new HardCodedClassifier();
    classifier.buildClassifier(train);//Currently, this does nothing
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(classifier, test);
    System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}

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From source file:expshell.ExpShell.java

/**
 * @param args the command line arguments
 * @throws java.lang.Exception/*  w ww  .jav a 2s  . c  o  m*/
 */
public static void main(String[] args) throws Exception {
    String file = "C:\\Users\\YH Jonathan Kwok\\Documents\\NetBeansProjects\\ExpShell\\src\\expshell\\iris.csv";

    DataSource source = new DataSource(file);
    Instances data = source.getDataSet();

    if (data.classIndex() == -1)
        data.setClassIndex(data.numAttributes() - 1);

    //Randomize it
    data.randomize(new Random(1));

    RemovePercentage rp = new RemovePercentage();
    rp.setPercentage(70);

    rp.setInputFormat(data);
    Instances training = Filter.useFilter(data, rp);

    rp.setInvertSelection(true);
    rp.setInputFormat(data);
    Instances test = Filter.useFilter(data, rp);

    //standardize the data
    Standardize filter = new Standardize();
    filter.setInputFormat(training);

    Instances newTest = Filter.useFilter(test, filter);
    Instances newTraining = Filter.useFilter(training, filter);

    //Part 5 - Now it's a knn
    Classifier knn = new NeuralClassifier();
    knn.buildClassifier(newTraining);
    Evaluation eval = new Evaluation(newTraining);
    eval.evaluateModel(knn, newTest);

    System.out.println(eval.toSummaryString("***** Overall results: *****", false));

}

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