Java Code Examples for weka.classifiers.trees.J48#buildClassifier()

The following examples show how to use weka.classifiers.trees.J48#buildClassifier() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.

Example 1

Source File: SaveModel.java From Hands-On-Artificial-Intelligence-with-Java-for-Beginners with MIT License

7 votes

/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    // TODO code application logic here
    try {
        DataSource src = new DataSource("/Users/admin/Documents/NetBeansProjects/SaveModel/segment-challenge.arff");
        Instances dt = src.getDataSet();
        dt.setClassIndex(dt.numAttributes() - 1);

        String[] options = new String[4];
        options[0] = "-C";
        options[1] = "0.1";
        options[2] = "-M";
        options[3] = "2";
        J48 mytree = new J48();
        mytree.setOptions(options);
        mytree.buildClassifier(dt);
        
        weka.core.SerializationHelper.write("/Users/admin/Documents/NetBeansProjects/SaveModel/myDT.model", mytree);
    }
    catch (Exception e) {
        System.out.println("Error!!!!\n" + e.getMessage());
    }
}

Example 2

Source File: MakingPredictions.java From Hands-On-Artificial-Intelligence-with-Java-for-Beginners with MIT License

5 votes

/**
 * @param args the command line arguments
 */
public static void main(String[] args) { 
    // TODO code application logic here
    try {
        DataSource src = new DataSource("/Users/admin/Documents/NetBeansProjects/MakingPredictions/segment-challenge.arff");
        Instances dt = src.getDataSet();
        dt.setClassIndex(dt.numAttributes() - 1);

        String[] options = new String[4];
        options[0] = "-C";
        options[1] = "0.1";
        options[2] = "-M";
        options[3] = "2";
        J48 mytree = new J48();
        mytree.setOptions(options);
        mytree.buildClassifier(dt);

        DataSource src1 = new DataSource("/Users/admin/Documents/NetBeansProjects/MakingPredictions/segment-test.arff");
        Instances tdt = src1.getDataSet();
        tdt.setClassIndex(tdt.numAttributes()-1);
         
        System.out.println("ActualClass \t ActualValue \t PredictedValue \t PredictedClass");
        for (int i = 0; i < tdt.numInstances(); i++)
        {
            String act = tdt.instance(i).stringValue(tdt.instance(i).numAttributes()-1);
            double actual = tdt.instance(i).classValue();
            Instance inst = tdt.instance(i);
            double predict = mytree.classifyInstance(inst);
            String pred = inst.toString(inst .numAttributes()-1);
            System.out.println(act + " \t\t " + actual + " \t\t " + predict + " \t\t " + pred);
        }
        
    } 
    catch (Exception e) {
        System.out.println(e.getCause());
    }
}

Example 3

Source File: DevelopClassifier.java From Hands-On-Artificial-Intelligence-with-Java-for-Beginners with MIT License

5 votes

/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    // TODO code application logic here
    try{
        DataSource src = new DataSource("/Users/admin/Documents/NetBeansProjects/DevelopClassifier/vote.arff");
        Instances dt = src.getDataSet();
        dt.setClassIndex(dt.numAttributes()-1);
        
        String[] options = new String[4];
        options[0] = "-C";
        options[1] = "0.1";
        options[2] = "-M";
        options[3] = "2";
        J48 tree = new J48();
        tree.setOptions(options);
        tree.buildClassifier(dt);
        System.out.println(tree.getCapabilities().toString());
        System.out.println(tree.graph());
        
        //uncomment the following three lines of code for Naive Bayes 
        NaiveBayes nb = new NaiveBayes();
        nb.buildClassifier(dt);
        System.out.println(nb.getCapabilities().toString());
        
        }        
    catch(Exception e){
        System.out.println("Error!!!!\n" + e.getMessage());
    }
}

Example 4

Source File: BookDecisionTree.java From Java-for-Data-Science with MIT License

5 votes

private J48 performTraining() {
        J48 j48 = new J48();
        String[] options = {"-U"};
//        Use unpruned tree. -U
        try {
            j48.setOptions(options);
            j48.buildClassifier(trainingData);
        } catch (Exception ex) {
            ex.printStackTrace();
        }
        return j48;
    }

Example 5

Source File: TestWekaJ48.java From Java-Data-Analysis with MIT License

5 votes

public static void main(String[] args) throws Exception {
    DataSource source = new DataSource("data/AnonFruit.arff");
    Instances instances = source.getDataSet();
    instances.setClassIndex(3);  // target attribute: (Sweet)
    
    J48 j48 = new J48();  // an extension of ID3
    j48.setOptions(new String[]{"-U"});  // use unpruned tree
    j48.buildClassifier(instances);

    for (Instance instance : instances) {
        double prediction = j48.classifyInstance(instance);
        System.out.printf("%4.0f%4.0f%n", 
                instance.classValue(), prediction);
    }
}

Example 6

Source File: ActivityRecognition.java From Machine-Learning-in-Java with MIT License

5 votes

public static void main(String[] args) throws Exception{
	
	String databasePath = "data/features.arff";
	
	// Load the data in arff format
	Instances data = new Instances(new BufferedReader(new FileReader(databasePath)));
	
	// Set class the last attribute as class
	data.setClassIndex(data.numAttributes() - 1);

	// Build a basic decision tree model
	String[] options = new String[]{};
	J48 model = new J48();
	model.setOptions(options);
	model.buildClassifier(data);
	
	// Output decision tree
	System.out.println("Decision tree model:\n"+model);
	
	// Output source code implementing the decision tree
	System.out.println("Source code:\n"+model.toSource("ActivityRecognitionEngine"));
	
	// Check accuracy of model using 10-fold cross-validation
	Evaluation eval = new Evaluation(data);
	eval.crossValidateModel(model, data, 10, new Random(1), new String[] {});
	System.out.println("Model performance:\n"+eval.toSummaryString());
	
	String[] activities = new String[]{"Walk", "Walk", "Walk", "Run", "Walk", "Run", "Run", "Sit", "Sit", "Sit"};
	DiscreteLowPass dlpFilter = new DiscreteLowPass(3);
	for(String str : activities){
		System.out.println(str +" -> "+ dlpFilter.filter(str));
	}
	
}

Example 7

Source File: ModelEvaluation.java From Hands-On-Artificial-Intelligence-with-Java-for-Beginners with MIT License

4 votes

/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    // TODO code application logic here
    try {
        DataSource src = new DataSource("/Users/admin/Documents/NetBeansProjects/ModelEvaluation/segment-challenge.arff");
        Instances dt = src.getDataSet();
        dt.setClassIndex(dt.numAttributes()- 1);

        String[] options = new String[4];
        options[0] = "-C";
        options[1] = "0.1";
        options[2] = "-M";
        options[3] = "2";
        J48 mytree = new J48();
        mytree.setOptions(options);
        mytree.buildClassifier(dt);
        
        Evaluation eval = new Evaluation(dt);
        Random rand = new Random(1);
        
        DataSource src1 = new DataSource("/Users/admin/Documents/NetBeansProjects/ModelEvaluation/segment-test.arff");
        Instances tdt = src1.getDataSet();
        tdt.setClassIndex(tdt.numAttributes() - 1);
        
        eval.evaluateModel(mytree, tdt);
        
        System.out.println(eval.toSummaryString("Evaluation results:\n", false));
            System.out.println("Correct % = " + eval.pctCorrect());
            System.out.println("Incorrect % = " + eval.pctIncorrect());
            System.out.println("kappa = " + eval.kappa());
            System.out.println("MAE = " + eval.meanAbsoluteError());
            System.out.println("RMSE = " + eval.rootMeanSquaredError());
            System.out.println("RAE = " + eval.relativeAbsoluteError());
            System.out.println("Precision = " + eval.precision(1));
            System.out.println("Recall = " + eval.recall(1));
            System.out.println("fMeasure = " + eval.fMeasure(1));
            System.out.println(eval.toMatrixString("=== Overall Confusion Matrix ==="));
    } catch (Exception e) {
        System.out.println("Error!!!!\n" + e.getMessage());
    }
}

Example 8

Source File: Clustering.java From java-ml-projects with Apache License 2.0

4 votes

@Override
public void start(Stage stage) throws Exception {
	loadData();
	tree = new J48();
	tree.buildClassifier(data);

	noClassificationChart = buildChart("No Classification (click to add new data)", buildSingleSeries());
	clusteredChart = buildChart("Clustered", buildClusteredSeries());
	realDataChart = buildChart("Real Data (+ Decision Tree classification for new data)", buildLabeledSeries());

	noClassificationChart.setOnMouseClicked(e -> {
		Axis<Number> xAxis = noClassificationChart.getXAxis();
		Axis<Number> yAxis = noClassificationChart.getYAxis();
		Point2D mouseSceneCoords = new Point2D(e.getSceneX(), e.getSceneY());
		double x = xAxis.sceneToLocal(mouseSceneCoords).getX();
		double y = yAxis.sceneToLocal(mouseSceneCoords).getY();
		Number xValue = xAxis.getValueForDisplay(x);
		Number yValue = yAxis.getValueForDisplay(y);
		reloadSeries(xValue, yValue);
	});

	Label lblDecisionTreeTitle = new Label("Decision Tree generated for the Iris dataset:");
	Text txtTree = new Text(tree.toString());
	String graph = tree.graph();
	SwingNode sw = new SwingNode();
	SwingUtilities.invokeLater(() -> {
		TreeVisualizer treeVisualizer = new TreeVisualizer(null, graph, new PlaceNode2());
		treeVisualizer.setPreferredSize(new Dimension(600, 500));
		sw.setContent(treeVisualizer);
	});

	Button btnRestore = new Button("Restore original data");
	Button btnSwapColors = new Button("Swap clustered chart colors");
	StackPane spTree = new StackPane(sw);
	spTree.setPrefWidth(300);
	spTree.setPrefHeight(350);
	VBox vbDecisionTree = new VBox(5, lblDecisionTreeTitle, new Separator(), spTree,
			new HBox(10, btnRestore, btnSwapColors));
	btnRestore.setOnAction(e -> {
		loadData();
		reloadSeries();
	});
	btnSwapColors.setOnAction(e -> swapClusteredChartSeriesColors());
	lblDecisionTreeTitle.setTextFill(Color.DARKRED);
	lblDecisionTreeTitle.setFont(Font.font(Font.getDefault().getFamily(), FontWeight.BOLD, FontPosture.ITALIC, 16));
	txtTree.setTranslateX(100);
	txtTree.setFont(Font.font(Font.getDefault().getFamily(), FontWeight.BOLD, FontPosture.ITALIC, 14));
	txtTree.setLineSpacing(1);
	txtTree.setTextAlignment(TextAlignment.LEFT);
	vbDecisionTree.setTranslateY(20);
	vbDecisionTree.setTranslateX(20);

	GridPane gpRoot = new GridPane();
	gpRoot.add(realDataChart, 0, 0);
	gpRoot.add(clusteredChart, 1, 0);
	gpRoot.add(noClassificationChart, 0, 1);
	gpRoot.add(vbDecisionTree, 1, 1);

	stage.setScene(new Scene(gpRoot));
	stage.setTitle("Íris dataset clustering and visualization");
	stage.show();
}