Example usage for weka.classifiers.trees REPTree graph

Introduction

In this page you can find the example usage for weka.classifiers.trees REPTree graph.

Prototype

@Override
public String graph() throws Exception 

Source Link

Document

Outputs the decision tree as a graph

Usage

From source file:lu.lippmann.cdb.datasetview.tabs.RegressionTreeTabView.java

License:Open Source License

/**
 * {@inheritDoc}//from ww w  . j a va  2 s. c om
 */
@SuppressWarnings("unchecked")
@Override
public void update0(final Instances dataSet) throws Exception {
    this.panel.removeAll();

    //final Object[] attrNames=WekaDataStatsUtil.getNumericAttributesNames(dataSet).toArray();
    final Object[] attrNames = WekaDataStatsUtil.getAttributeNames(dataSet).toArray();
    final JComboBox xCombo = new JComboBox(attrNames);
    xCombo.setBorder(new TitledBorder("Attribute to evaluate"));

    final JXPanel comboPanel = new JXPanel();
    comboPanel.setLayout(new GridLayout(1, 2));
    comboPanel.add(xCombo);
    final JXButton jxb = new JXButton("Compute");
    comboPanel.add(jxb);
    this.panel.add(comboPanel, BorderLayout.NORTH);

    jxb.addActionListener(new ActionListener() {
        @Override
        public void actionPerformed(ActionEvent e) {
            try {
                if (gv != null)
                    panel.remove((Component) gv);

                dataSet.setClassIndex(xCombo.getSelectedIndex());

                final REPTree rt = new REPTree();
                rt.setNoPruning(true);
                //rt.setMaxDepth(3);
                rt.buildClassifier(dataSet);

                /*final M5P rt=new M5P();
                rt.buildClassifier(dataSet);*/

                final Evaluation eval = new Evaluation(dataSet);
                double[] d = eval.evaluateModel(rt, dataSet);
                System.out.println("PREDICTED -> " + FormatterUtil.buildStringFromArrayOfDoubles(d));
                System.out.println(eval.errorRate());
                System.out.println(eval.sizeOfPredictedRegions());
                System.out.println(eval.toSummaryString("", true));

                final GraphWithOperations gwo = GraphUtil
                        .buildGraphWithOperationsFromWekaRegressionString(rt.graph());
                final DecisionTree dt = new DecisionTree(gwo, eval.errorRate());

                gv = DecisionTreeToGraphViewHelper.buildGraphView(dt, eventPublisher, commandDispatcher);
                gv.addMetaInfo("Size=" + dt.getSize(), "");
                gv.addMetaInfo("Depth=" + dt.getDepth(), "");

                gv.addMetaInfo("MAE=" + FormatterUtil.DECIMAL_FORMAT.format(eval.meanAbsoluteError()) + "", "");
                gv.addMetaInfo("RMSE=" + FormatterUtil.DECIMAL_FORMAT.format(eval.rootMeanSquaredError()) + "",
                        "");

                final JCheckBox toggleDecisionTreeDetails = new JCheckBox("Toggle details");
                toggleDecisionTreeDetails.addActionListener(new ActionListener() {
                    @Override
                    public void actionPerformed(ActionEvent e) {
                        if (!tweakedGraph) {
                            final Object[] mapRep = WekaDataStatsUtil
                                    .buildNodeAndEdgeRepartitionMap(dt.getGraphWithOperations(), dataSet);
                            gv.updateVertexShapeTransformer((Map<CNode, Map<Object, Integer>>) mapRep[0]);
                            gv.updateEdgeShapeRenderer((Map<CEdge, Float>) mapRep[1]);
                        } else {
                            gv.resetVertexAndEdgeShape();
                        }
                        tweakedGraph = !tweakedGraph;
                    }
                });
                gv.addMetaInfoComponent(toggleDecisionTreeDetails);

                /*final JButton openInEditorButton = new JButton("Open in editor");
                openInEditorButton.addActionListener(new ActionListener() {
                   @Override
                   public void actionPerformed(ActionEvent e) {
                       GraphUtil.importDecisionTreeInEditor(dtFactory, dataSet, applicationContext, eventPublisher, commandDispatcher);
                   }
                });
                this.gv.addMetaInfoComponent(openInEditorButton);*/

                final JButton showTextButton = new JButton("In text");
                showTextButton.addActionListener(new ActionListener() {
                    @Override
                    public void actionPerformed(ActionEvent e) {
                        JOptionPane.showMessageDialog(null, graphDsl.getDslString(dt.getGraphWithOperations()));
                    }
                });
                gv.addMetaInfoComponent(showTextButton);

                panel.add(gv.asComponent(), BorderLayout.CENTER);
            } catch (Exception e1) {
                e1.printStackTrace();
                panel.add(new JXLabel("Error during computation: " + e1.getMessage()), BorderLayout.CENTER);
            }

        }
    });
}

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From source file:lu.lippmann.cdb.dt.RegressionTreeFactory.java

License:Open Source License

/**
 * Main method./*w w  w. j ava2  s .c o  m*/
 * @param args command line arguments
 */
public static void main(final String[] args) {
    try {
        final String f = "./samples/csv/uci/winequality-red.csv";
        //final String f="./samples/arff/UCI/crimepredict.arff";
        final Instances dataSet = WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(new File(f));
        System.out.println(dataSet.classAttribute().isNumeric());

        final REPTree rt = new REPTree();
        rt.setMaxDepth(3);
        rt.buildClassifier(dataSet);

        System.out.println(rt);

        //System.out.println(rt.graph());

        final GraphWithOperations gwo = GraphUtil.buildGraphWithOperationsFromWekaRegressionString(rt.graph());
        System.out.println(gwo);
        System.out.println(new ASCIIGraphDsl().getDslString(gwo));

        final Evaluation eval = new Evaluation(dataSet);

        /*Field privateStringField = Evaluation.class.getDeclaredField("m_CoverageStatisticsAvailable");
        privateStringField.setAccessible(true);
        //privateStringField.get
        boolean fieldValue = privateStringField.getBoolean(eval);
        System.out.println("fieldValue = " + fieldValue);*/

        double[] d = eval.evaluateModel(rt, dataSet);
        System.out.println("PREDICTED -> " + FormatterUtil.buildStringFromArrayOfDoubles(d));

        System.out.println(eval.errorRate());
        System.out.println(eval.sizeOfPredictedRegions());

        System.out.println(eval.toSummaryString("", true));

        /*final String f2="./samples/csv/salary.csv";
        final Instances dataSet2=WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(new File(f2));
                
        final J48 j48=new J48();
        j48.buildClassifier(dataSet2);
        System.out.println(j48.graph());
        final GraphWithOperations gwo2=GraphUtil.buildGraphWithOperationsFromWekaString(j48.graph(),false);
        System.out.println(gwo2);*/

        System.out.println(new DecisionTree(gwo, eval.errorRate()));
    } catch (Exception e) {
        e.printStackTrace();
    }
}

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