NaiveBayesClassification.java

package naivebayesclassification;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.util.ArrayList;
import javafx.application.Application;
import javafx.event.ActionEvent;
import javafx.event.EventHandler;
import javafx.geometry.Insets;
import javafx.scene.Scene;
import javafx.scene.control.Button;
import javafx.scene.control.Label;
import javafx.scene.control.Tab;
import javafx.scene.control.TabPane;
import javafx.scene.control.TextArea;
import javafx.scene.control.TextField;
import javafx.scene.layout.ColumnConstraints;
import javafx.scene.layout.GridPane;
import javafx.scene.layout.Priority;
import javafx.scene.layout.RowConstraints;
import javafx.scene.layout.StackPane;
import javafx.stage.DirectoryChooser;
import javafx.stage.FileChooser;
import javafx.stage.FileChooser.ExtensionFilter;
import javafx.stage.Stage;

public class NaiveBayesClassification extends Application {

    private TextArea taInfoProses, taDataTraining, taTarget, taToken, taModel, taDatatesting, taHasiltesting, taTestCase;
    private final FileChooser fileChooser = new FileChooser();
    private File fileDataTraining = null;
    private File fileDataTesting = null;
    private String sDataTraining = new String();
    private String sDataTesting = new String();
    private final String DATA_TRAINING = "data_training";
    private final String DATA_TESTING = "data_testing";
    private ArrayList<String> target = null;
    private ArrayList<Data> listDataTraining = null;
    private ArrayList<Data> listDataTesting = null;

    @Override
    public void start(Stage primaryStage) {
        StackPane root = new StackPane();
        GridPane gridPane = new GridPane();
        gridPane.setPadding(new Insets(10, 10, 10, 10));
        gridPane.setHgap(5);
        gridPane.setVgap(10);

        //col constraints
        ColumnConstraints col1 = new ColumnConstraints();
        col1.setHgrow(Priority.NEVER);
        ColumnConstraints col2 = new ColumnConstraints();
        col2.setHgrow(Priority.ALWAYS);
        ColumnConstraints col3 = new ColumnConstraints();
        col3.setHgrow(Priority.NEVER);
        gridPane.getColumnConstraints().addAll(col1, col2, col3);

        //row constraints
        RowConstraints row1 = new RowConstraints();
        row1.setVgrow(Priority.NEVER);
        RowConstraints row2 = new RowConstraints();
        row2.setVgrow(Priority.ALWAYS);
        gridPane.getRowConstraints().addAll(row1, row1, row1, row1, row1, row2);

        Label labelWorkspace = new Label("Workspace");
        gridPane.add(labelWorkspace, 0, 0);
        TextField tfWorkspace = new TextField();
        gridPane.add(tfWorkspace, 1, 0);
        Button buttonOpenWorkspace = new Button("Open");
        gridPane.add(buttonOpenWorkspace, 2, 0);

        buttonOpenWorkspace.setOnAction(new EventHandler<ActionEvent>() {
            @Override
            public void handle(final ActionEvent e) {
                DirectoryChooser chooser = new DirectoryChooser();
                chooser.setTitle("Workspace Naive Bayes");
                File selectedDirectory = chooser.showDialog(primaryStage);
                if (selectedDirectory != null) {
                    tfWorkspace.setText(selectedDirectory.getPath().toString());
                    //openFile(file);
                }
            }
        });

        Label labelDataTraining = new Label("DataTraining");
        gridPane.add(labelDataTraining, 0, 1);
        TextField tfDataTraining = new TextField();
        gridPane.add(tfDataTraining, 1, 1);
        Button buttonOpenDataTraining = new Button("Browse Data Training");
        gridPane.add(buttonOpenDataTraining, 2, 1);

        buttonOpenDataTraining.setOnAction(new EventHandler<ActionEvent>() {
            @Override
            public void handle(final ActionEvent e) {
                fileChooser.setTitle("Open Data Training");
                fileChooser.getExtensionFilters().addAll(
                        new ExtensionFilter("Text Files", "*.txt"),
                        new ExtensionFilter("All Files", "*.*"));
                File file = fileChooser.showOpenDialog(primaryStage);
                if (file != null) {
                    tfDataTraining.setText(file.getPath().toString());
                    //set file data training;
                    fileDataTraining = file;
                }
            }
        });

        Label labelDataTesting = new Label("DataTesting");
        gridPane.add(labelDataTesting, 0, 2);
        TextField tfDataTesting = new TextField();
        gridPane.add(tfDataTesting, 1, 2);
        Button buttonOpenDataTesting = new Button("Browse Data Testing");
        gridPane.add(buttonOpenDataTesting, 2, 2);

        buttonOpenDataTesting.setOnAction(new EventHandler<ActionEvent>() {
            @Override
            public void handle(final ActionEvent e) {
                fileChooser.setTitle("Open Data Testing");
                fileChooser.getExtensionFilters().addAll(
                        new ExtensionFilter("Text Files", "*.txt"),
                        new ExtensionFilter("All Files", "*.*"));
                File file = fileChooser.showOpenDialog(primaryStage);
                if (file != null) {
                    tfDataTesting.setText(file.getPath().toString());
                    //set file data testing;
                    fileDataTesting = file;
                }
            }
        });

        Button buttonTrainingNaiveBayes = new Button("Training Naive Bayes Classification");
        gridPane.add(buttonTrainingNaiveBayes, 1, 3);
        buttonTrainingNaiveBayes.setOnAction(new EventHandler<ActionEvent>() {
            @Override
            public void handle(final ActionEvent e) {
                prosesTrainingNaiveBayes();
            }
        });

        Button buttonTestingNaiveBayes = new Button("Testing Naive Bayes Classification");
        gridPane.add(buttonTestingNaiveBayes, 1, 4);
        buttonTestingNaiveBayes.setOnAction(new EventHandler<ActionEvent>() {
            @Override
            public void handle(final ActionEvent e) {
                ///
            }
        });

        //Binding    
        buttonOpenWorkspace.prefWidthProperty().bind(buttonOpenDataTraining.widthProperty());
        buttonOpenDataTesting.prefWidthProperty().bind(buttonOpenDataTraining.widthProperty());
        buttonTrainingNaiveBayes.prefWidthProperty().bind(tfDataTesting.widthProperty());
        buttonTestingNaiveBayes.prefWidthProperty().bind(tfDataTesting.widthProperty());

        TabPane tabPane = new TabPane();
        gridPane.add(tabPane, 0, 5, 3, 2);

        //initialize text area taInfoProses,taTarget,taToken,taModel,taHasiltesting,taTestCase
        taInfoProses = new TextArea();
        taDataTraining = new TextArea();
        taTarget = new TextArea();
        taToken = new TextArea();
        taModel = new TextArea();
        taDatatesting = new TextArea();
        taHasiltesting = new TextArea();
        taTestCase = new TextArea();

        taInfoProses.setWrapText(true);
        taDataTraining.setWrapText(true);
        taTarget.setWrapText(true);
        taToken.setWrapText(true);
        taModel.setWrapText(true);
        taDatatesting.setWrapText(true);
        taHasiltesting.setWrapText(true);
        taTestCase.setWrapText(true);

        Tab tab1 = new Tab();
        tab1.setText("Info Proses");
        tab1.setContent(taInfoProses);

        Tab tab2 = new Tab();
        tab2.setText("Data Training");
        tab2.setContent(taDataTraining);

        Tab tab3 = new Tab();
        tab3.setText("Target");
        tab3.setContent(taTarget);

        Tab tab4 = new Tab();
        tab4.setText("Token");
        tab4.setContent(taToken);

        Tab tab5 = new Tab();
        tab5.setText("Model");
        tab5.setContent(taModel);

        Tab tab6 = new Tab();
        tab6.setText("Data Testing");
        tab6.setContent(taDatatesting);

        Tab tab7 = new Tab();
        tab7.setText("Hasil Testing");
        tab7.setContent(taHasiltesting);

        Tab tab8 = new Tab();
        tab8.setText("Test Manual");
        tab8.setContent(taTestCase);

        tabPane.getSelectionModel().select(0);
        tabPane.getTabs().addAll(tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8);

        root.getChildren().add(gridPane);

        Scene scene = new Scene(root, 1024, 768);

        primaryStage.setTitle("Naive Bayes Classification");
        primaryStage.setScene(scene);
        primaryStage.show();
    }

    //methods
    private ArrayList<Data> readData(File file, String jenisData) {
        ArrayList<Data> listData = null;
        try {
            listData = new ArrayList<Data>();
            StringBuffer contents = new StringBuffer();
            BufferedReader reader = new BufferedReader(new FileReader(file));
            String sLine = null;
            String separator = " ";

            // repeat until all lines is read
            while ((sLine = reader.readLine()) != null) {
                String[] arrLine = sLine.split(separator);
                String sTarget = arrLine[0];
                String sURL = arrLine[1];
                Data data = new Data(sTarget, sURL);
                listData.add(data);
                contents.append(sTarget + "<---" + sURL + "\n");
            }
            if (listData.size() > 0) {
                if (jenisData.equals(this.DATA_TRAINING)) {
                    sDataTraining = contents.toString();
                    taDataTraining.setText(sDataTraining);
                } else if (jenisData.equals(this.DATA_TESTING)) {
                    sDataTesting = contents.toString();
                    taDatatesting.setText(sDataTesting);
                }
            }
            reader.close();
        } catch (Exception ex) {
            ex.printStackTrace();
        }
        return listData;
    }

    private ArrayList<String> readTarget(ArrayList<Data> listData) {
        ArrayList<String> listTarget = null;
        if (listData != null) {
            listTarget = new ArrayList<String>();
            for (int i = 0; i < listData.size(); i++) {
                String sTarget = listData.get(i).getTarget().toLowerCase();
                boolean ada = false;
                for (int j = 0; j < listTarget.size(); j++) {
                    if (listTarget.get(j).equals(sTarget)) {
                        ada = true;
                        break;
                    }
                }
                if (!ada) {
                    listTarget.add(sTarget);
                }
            }
            String sTarget = "";
            for(int i=0;i<listTarget.size();i++){
                sTarget = (sTarget+listTarget.get(i)+"\n");
            }
            taTarget.setText(sTarget);
                            
        }
        return listTarget;
    }

    private void prosesTrainingNaiveBayes() {
        if (fileDataTraining != null) {
            this.listDataTraining = readData(fileDataTraining, this.DATA_TRAINING);
            if (this.listDataTraining != null) {
                //initialize target
                target = readTarget(this.listDataTraining);
                System.out.println(target.toString());
            }
        }
    }

    public static void main(String[] args) {
        launch(args);
    }

}