List of usage examples for weka.gui.visualize ThresholdVisualizePanel setROCString
public void setROCString(String str)
From source file:adams.gui.menu.ROC.java
License:Open Source License
/** * Launches the functionality of the menu item. *///from w w w .ja v a 2s.co m @Override public void launch() { File file; if (m_Parameters.length == 0) { // choose file int retVal = m_FileChooser.showOpenDialog(null); if (retVal != JFileChooser.APPROVE_OPTION) return; file = m_FileChooser.getSelectedFile(); } else { file = new PlaceholderFile(m_Parameters[0]).getAbsoluteFile(); m_FileChooser.setSelectedFile(file); } // create plot Instances result; try { result = m_FileChooser.getLoader().getDataSet(); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error loading file '" + file + "':\n" + adams.core.Utils.throwableToString(e)); return; } result.setClassIndex(result.numAttributes() - 1); ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(ThresholdCurve.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) cp[n] = true; try { tempd.setConnectPoints(cp); vmc.addPlot(tempd); } catch (Exception e) { GUIHelper.showErrorMessage(getOwner(), "Error adding plot:\n" + adams.core.Utils.throwableToString(e)); return; } ChildFrame frame = createChildFrame(vmc, GUIHelper.getDefaultDialogDimension()); frame.setTitle(frame.getTitle() + " - " + file); }
From source file:com.sliit.views.DataVisualizerPanel.java
void getRocCurve() { try {//from ww w. ja v a 2 s. c o m Instances data; data = new Instances(new BufferedReader(new FileReader(datasetPathText.getText()))); data.setClassIndex(data.numAttributes() - 1); // train classifier Classifier cl = new NaiveBayes(); Evaluation eval = new Evaluation(data); eval.crossValidateModel(cl, data, 10, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) { cp[n] = true; } tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); // display curve String plotName = vmc.getName(); final javax.swing.JFrame jf = new javax.swing.JFrame("Weka Classifier Visualize: " + plotName); jf.setSize(500, 400); jf.getContentPane().setLayout(new BorderLayout()); jf.getContentPane().add(vmc, BorderLayout.CENTER); jf.addWindowListener(new java.awt.event.WindowAdapter() { public void windowClosing(java.awt.event.WindowEvent e) { jf.dispose(); } }); jf.setVisible(true); } catch (IOException ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:com.sliit.views.KNNView.java
void getRocCurve() { try {//w w w . j a v a2 s .c o m Instances data; data = new Instances(new BufferedReader(new java.io.FileReader(PredictorPanel.modalText.getText()))); data.setClassIndex(data.numAttributes() - 1); // train classifier Classifier cl = new NaiveBayes(); Evaluation eval = new Evaluation(data); eval.crossValidateModel(cl, data, 10, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) { cp[n] = true; } tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); rocPanel.removeAll(); rocPanel.add(vmc, "vmc", 0); rocPanel.revalidate(); } catch (IOException ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:com.sliit.views.SVMView.java
/** * draw ROC curve/*from w w w. j av a 2 s .c om*/ */ void getRocCurve() { try { Instances data; data = new Instances(new BufferedReader(new FileReader(PredictorPanel.modalText.getText()))); data.setClassIndex(data.numAttributes() - 1); //train classifier Classifier cl = new NaiveBayes(); Evaluation eval = new Evaluation(data); eval.crossValidateModel(cl, data, 10, new Random(1)); // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); // plot curve ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) { cp[n] = true; } tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); // rocPanel.removeAll(); // rocPanel.add(vmc, "vmc", 0); // rocPanel.revalidate(); } catch (IOException ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(DataVisualizerPanel.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:meka.gui.explorer.classify.ShowPrecisionRecall.java
License:Open Source License
/** * Creates a panel displaying the ROC data. * * @param data the threshold curve data * @param title the title of the plot * @return the panel/*from www. j ava2 s . com*/ * @throws Exception if plot generation fails */ protected ThresholdVisualizePanel createPanel(Instances data, String title) throws Exception { ThresholdVisualizePanel result = super.createPanel(data, title); result.setROCString("PRC area: " + Utils.doubleToString(ThresholdCurve.getPRCArea(data), 3)); result.setUpComboBoxes(result.getInstances()); setComboBoxIndices(data, result); return result; }
From source file:meka.gui.explorer.classify.ShowROC.java
License:Open Source License
/** * Creates a panel displaying the ROC data. * * @param data the threshold curve data * @param title the title of the plot * @return the panel/*from ww w. j av a 2 s. c o m*/ * @throws Exception if plot generation fails */ protected ThresholdVisualizePanel createPanel(Instances data, String title) throws Exception { ThresholdVisualizePanel result = super.createPanel(data, title); result.setROCString("AUC: " + Utils.doubleToString(ThresholdCurve.getROCArea(data), 3)); result.setUpComboBoxes(result.getInstances()); return result; }
From source file:meka.gui.guichooser.PrecisionRecallCurve.java
License:Open Source License
/** * Called by the menu items action listener. *//*from w w w . j a v a 2 s . c om*/ @Override protected void launch() { m_FileChooser = GUIHelper.newConverterFileChooser(); // choose file int retVal = m_FileChooser.showOpenDialog(null); if (retVal != JFileChooser.APPROVE_OPTION) return; File file = m_FileChooser.getSelectedFile(); // create plot Instances data; try { data = m_FileChooser.getLoader().getDataSet(); } catch (Exception e) { JOptionPane.showMessageDialog(null, "Error loading file '" + file + "':\n" + e, "Error", JOptionPane.ERROR_MESSAGE); e.printStackTrace(); return; } data.setClassIndex(data.numAttributes() - 1); ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under PRC = " + Utils.doubleToString(ThresholdCurve.getPRCArea(data), 4) + ")"); vmc.setName(data.relationName()); PlotData2D tempd = new PlotData2D(data); tempd.setPlotName(data.relationName()); tempd.m_displayAllPoints = true; // specify which points are connected boolean[] cp = new boolean[data.numInstances()]; for (int n = 1; n < cp.length; n++) cp[n] = true; try { tempd.setConnectPoints(cp); vmc.addPlot(tempd); if (data.attribute(ThresholdCurve.RECALL_NAME) != null) vmc.setXIndex(data.attribute(ThresholdCurve.RECALL_NAME).index()); if (data.attribute(ThresholdCurve.PRECISION_NAME) != null) vmc.setYIndex(data.attribute(ThresholdCurve.PRECISION_NAME).index()); } catch (Exception e) { JOptionPane.showMessageDialog(null, "Error adding plot:\n" + e, "Error", JOptionPane.ERROR_MESSAGE); e.printStackTrace(); return; } MekaFrame frame = new MekaFrame(); frame.setTitle(getName()); frame.setDefaultCloseOperation(MekaFrame.DISPOSE_ON_CLOSE); frame.getContentPane().setLayout(new BorderLayout()); frame.getContentPane().add(vmc); frame.setSize(800, 600); frame.setLocationRelativeTo(null); frame.setVisible(true); }
From source file:meka.gui.guichooser.ROC.java
License:Open Source License
/** * Called by the menu items action listener. *//*from w w w . j a va 2 s . c o m*/ @Override protected void launch() { m_FileChooser = GUIHelper.newConverterFileChooser(); // choose file int retVal = m_FileChooser.showOpenDialog(null); if (retVal != JFileChooser.APPROVE_OPTION) return; File file = m_FileChooser.getSelectedFile(); // create plot Instances data; try { data = m_FileChooser.getLoader().getDataSet(); } catch (Exception e) { JOptionPane.showMessageDialog(null, "Error loading file '" + file + "':\n" + e, "Error", JOptionPane.ERROR_MESSAGE); e.printStackTrace(); return; } data.setClassIndex(data.numAttributes() - 1); ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(ThresholdCurve.getROCArea(data), 4) + ")"); vmc.setName(data.relationName()); PlotData2D tempd = new PlotData2D(data); tempd.setPlotName(data.relationName()); tempd.m_displayAllPoints = true; // specify which points are connected boolean[] cp = new boolean[data.numInstances()]; for (int n = 1; n < cp.length; n++) cp[n] = true; try { tempd.setConnectPoints(cp); vmc.addPlot(tempd); if (data.attribute(ThresholdCurve.FP_RATE_NAME) != null) vmc.setXIndex(data.attribute(ThresholdCurve.FP_RATE_NAME).index()); if (data.attribute(ThresholdCurve.TP_RATE_NAME) != null) vmc.setYIndex(data.attribute(ThresholdCurve.TP_RATE_NAME).index()); } catch (Exception e) { JOptionPane.showMessageDialog(null, "Error adding plot:\n" + e, "Error", JOptionPane.ERROR_MESSAGE); e.printStackTrace(); return; } MekaFrame frame = new MekaFrame(); frame.setTitle(getName()); frame.setDefaultCloseOperation(MekaFrame.DISPOSE_ON_CLOSE); frame.getContentPane().setLayout(new BorderLayout()); frame.getContentPane().add(vmc); frame.setSize(800, 600); frame.setLocationRelativeTo(null); frame.setVisible(true); }
From source file:miRdup.WekaModule.java
License:Open Source License
public static void rocCurve(Evaluation eval) { try {/* w w w.jav a 2 s . co m*/ // generate curve ThresholdCurve tc = new ThresholdCurve(); int classIndex = 0; Instances result = tc.getCurve(eval.predictions(), classIndex); result.toString(); // plot curve ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); vmc.setROCString("(Area under ROC = " + Utils.doubleToString(tc.getROCArea(result), 4) + ")"); vmc.setName(result.relationName()); PlotData2D tempd = new PlotData2D(result); tempd.setPlotName(result.relationName()); tempd.addInstanceNumberAttribute(); // specify which points are connected boolean[] cp = new boolean[result.numInstances()]; for (int n = 1; n < cp.length; n++) { cp[n] = true; } tempd.setConnectPoints(cp); // add plot vmc.addPlot(tempd); // result.toString(); // display curve String plotName = vmc.getName(); final javax.swing.JFrame jf = new javax.swing.JFrame("Weka Classifier Visualize: " + plotName); jf.setSize(500, 400); jf.getContentPane().setLayout(new BorderLayout()); jf.getContentPane().add(vmc, BorderLayout.CENTER); jf.addWindowListener(new java.awt.event.WindowAdapter() { public void windowClosing(java.awt.event.WindowEvent e) { jf.dispose(); } }); jf.setVisible(true); System.out.println(""); } catch (Exception e) { e.printStackTrace(); } }