Examples of weka.classifiers.Classifier

weka.classifiers.Classifier
Abstract classifier. All schemes for numeric or nominal prediction in Weka extend this class. Note that a classifier MUST either implement distributionForInstance() or classifyInstance(). @author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 5536 $

  argsR = new String [args.length-10];
  for (int j = 10; j < args.length; j++) {
    argsR[j-10] = args[j];
  }
      }
      Classifier c = Classifier.forName(args[9], argsR);
      KDDataGenerator dataGen = new KDDataGenerator();
      dataGen.setKernelBandwidth(bandWidth);
      bv.setDataGenerator(dataGen);
      bv.setNumSamplesPerRegion(loc);
      bv.setGeneratorSamplesBase(base);

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    Vector<Performance>    performances;
    PointDouble      values;
    Instances      data;
    Evaluation      eval;
    PointDouble      result;
    Classifier      classifier;
    Filter      filter;
    int        size;
    boolean      cached;
    boolean      allCached;
    Performance      p1;

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      if (m_evaluateThread == null) {
  m_evaluateThread = new Thread() {
      public void run() {
        boolean errorOccurred = false;
//        final String oldText = m_visual.getText();
        Classifier classifier = ce.getClassifier();
        try {
    //if (ce.getSetNumber() == 1) {
          if (ce.getGroupIdentifier() != m_currentBatchIdentifier) {
                  if (ce.getTrainSet().getDataSet() == null ||
                      ce.getTrainSet().getDataSet().numInstances() == 0) {
                    // we have no training set to estimate majority class
                    // or mean of target from
                    m_eval = new Evaluation(ce.getTestSet().getDataSet());
                    m_eval.useNoPriors();
                  } else {
                    m_eval = new Evaluation(ce.getTrainSet().getDataSet());
                  }


//      m_classifier = ce.getClassifier();
      if (m_visualizableErrorListeners.size() > 0) {
        m_predInstances = 
          weka.gui.explorer.ClassifierPanel.
          setUpVisualizableInstances(new Instances(ce.getTestSet().getDataSet()));
        m_plotShape = new FastVector();
        m_plotSize = new FastVector();
      }
      
      m_currentBatchIdentifier = ce.getGroupIdentifier();
                  m_setsComplete = 0;
    }
//    if (ce.getSetNumber() <= ce.getMaxSetNumber()) {
          if (m_setsComplete < ce.getMaxSetNumber()) {
      if (ce.getTrainSet().getDataSet() != null &&
                      ce.getTrainSet().getDataSet().numInstances() > 0) {
                    // set the priors
                    m_eval.setPriors(ce.getTrainSet().getDataSet());
                  }
      
//      m_visual.setText("Evaluating ("+ce.getSetNumber()+")...");
      if (m_logger != null) {
        m_logger.statusMessage(statusMessagePrefix()
             + Messages.getInstance().getString("ClassifierPerformanceEvaluator_AcceptClassifier_Visual_SetText_Text_First") + ce.getSetNumber()
             + Messages.getInstance().getString("ClassifierPerformanceEvaluator_AcceptClassifier_Visual_SetText_Text_Second"));
      }
      m_visual.setAnimated();
      /*
      m_eval.evaluateModel(ce.getClassifier(), 
      ce.getTestSet().getDataSet()); */
      for (int i = 0; i < ce.getTestSet().getDataSet().numInstances(); i++) {
        Instance temp = ce.getTestSet().getDataSet().instance(i);
        weka.gui.explorer.ClassifierPanel.
        processClassifierPrediction(temp, ce.getClassifier(),
            m_eval, m_predInstances, m_plotShape,
            m_plotSize);
      }
      
      m_setsComplete++;
    }
    
    if (ce.getSetNumber() == ce.getMaxSetNumber()) {
                  //      System.err.println(m_eval.toSummaryString());
      // m_resultsString.append(m_eval.toSummaryString());
      // m_outText.setText(m_resultsString.toString());
      String textTitle = classifier.getClass().getName();
      String textOptions = "";
      if (classifier instanceof OptionHandler) {
               textOptions = 
                 Utils.joinOptions(((OptionHandler)classifier).getOptions()); 
      }

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   * @return the configured FilteredClassifier
   */
  protected FilteredClassifier getFilteredClassifier() {
    FilteredClassifier  result;
    Filter    filter;
    Classifier    cls;
    
    result = new FilteredClassifier();
    
    // set filter
    filter = getFilter();

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   *
   * @return a <code>FastVector</code> containing the predictions.
   */
  protected FastVector useClassifier() throws Exception {


    Classifier dc = null;
    int tot = m_Instances.numInstances();
    int mid = tot / 2;
    Instances train = null;
    Instances test = null;
    try {

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    m_ClassifierEditor.addPropertyChangeListener(new PropertyChangeListener() {
      public void propertyChange(PropertyChangeEvent e) {
        m_StartBut.setEnabled(true);
        // Check capabilities
        Capabilities currentFilter = m_ClassifierEditor.getCapabilitiesFilter();
        Classifier classifier = (Classifier) m_ClassifierEditor.getValue();
        Capabilities currentSchemeCapabilities =  null;
        if (classifier != null && currentFilter != null && 
            (classifier instanceof CapabilitiesHandler)) {
          currentSchemeCapabilities = ((CapabilitiesHandler)classifier).getCapabilities();

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    int testMode = 0;
    int numFolds = 10;
          double percent = 66;
    int classIndex = m_ClassCombo.getSelectedIndex();
    Classifier classifier = (Classifier) m_ClassifierEditor.getValue();
    Classifier template = null;
    try {
      template = Classifier.makeCopy(classifier);
    } catch (Exception ex) {
      m_Log.logMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_LogMessage_Text_First") + ex.getMessage());
    }
    Classifier fullClassifier = null;
    StringBuffer outBuff = new StringBuffer();
    String name = (new SimpleDateFormat("HH:mm:ss - "))
    .format(new Date());
    String cname = classifier.getClass().getName();
    if (cname.startsWith("weka.classifiers.")) {
      name += cname.substring("weka.classifiers.".length());
    } else {
      name += cname;
    }
          String cmd = m_ClassifierEditor.getValue().getClass().getName();
          if (m_ClassifierEditor.getValue() instanceof OptionHandler)
            cmd += " " + Utils.joinOptions(((OptionHandler) m_ClassifierEditor.getValue()).getOptions());
    Evaluation eval = null;
    try {
      if (m_CVBut.isSelected()) {
        testMode = 1;
        numFolds = Integer.parseInt(m_CVText.getText());
        if (numFolds <= 1) {
    throw new Exception(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Exception_Text_First"));
        }
      } else if (m_PercentBut.isSelected()) {
        testMode = 2;
        percent = Double.parseDouble(m_PercentText.getText());
        if ((percent <= 0) || (percent >= 100)) {
    throw new Exception(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Exception_Text_Second"));
        }
      } else if (m_TrainBut.isSelected()) {
        testMode = 3;
      } else if (m_TestSplitBut.isSelected()) {
        testMode = 4;
        // Check the test instance compatibility
        if (source == null) {
    throw new Exception(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Exception_Text_Third"));
        }
        if (!inst.equalHeaders(userTestStructure)) {
    throw new Exception(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Exception_Text_Fourth"));
        }
              userTestStructure.setClassIndex(classIndex);
      } else {
        throw new Exception(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Exception_Text_Fifth"));
      }
      inst.setClassIndex(classIndex);


      // set up the structure of the plottable instances for 
      // visualization
            if (saveVis) {
              predInstances = setUpVisualizableInstances(inst);
              predInstances.setClassIndex(inst.classIndex()+1);
            } 


      // Output some header information
      m_Log.logMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_LogMessage_Text_Second") + cname);
      m_Log.logMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_LogMessage_Text_Third") + cmd);
      if (m_Log instanceof TaskLogger) {
        ((TaskLogger)m_Log).taskStarted();
      }
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_First"));
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Second") + cname);
      if (classifier instanceof OptionHandler) {
        String [] o = ((OptionHandler) classifier).getOptions();
        outBuff.append(" " + Utils.joinOptions(o));
      }
      outBuff.append("\n");
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Fourth") + inst.relationName() + '\n');
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Sixth") + inst.numInstances() + '\n');
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Eigth") + inst.numAttributes() + '\n');
      if (inst.numAttributes() < 100) {
        for (int i = 0; i < inst.numAttributes(); i++) {
    outBuff.append("              " + inst.attribute(i).name()
             + '\n');
        }
      } else {
        outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Twelveth"));
      }


      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Thirteenth"));
      switch (testMode) {
        case 3: // Test on training
    outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Fourteenth"));
    break;
        case 1: // CV mode
    outBuff.append("" + numFolds + Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Sixteenth"));
    break;
        case 2: // Percent split
    outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Seventeenth") + percent
        + Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Eighteenth"));
    break;
        case 4: // Test on user split
    if (source.isIncremental())
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Nineteenth"));
    else
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_Twentyth")
          + source.getDataSet().numInstances() + Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_TwentyFirst"));
    break;
      }
            if (costMatrix != null) {
               outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_TwentySecond"))
               .append(costMatrix.toString()).append("\n");
            }
      outBuff.append("\n");
      m_History.addResult(name, outBuff);
      m_History.setSingle(name);
      
      // Build the model and output it.
      if (outputModel || (testMode == 3) || (testMode == 4)) {
        m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Second"));


        trainTimeStart = System.currentTimeMillis();
        classifier.buildClassifier(inst);
        trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
      }


      if (outputModel) {
        outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_TwentySixth"));
        outBuff.append(classifier.toString() + "\n");
        outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_TwentyEighth") +
           Utils.doubleToString(trainTimeElapsed / 1000.0,2)
           + " " + Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_TwentyNineth"));
        m_History.updateResult(name);
        if (classifier instanceof Drawable) {
    grph = null;
    try {
      grph = ((Drawable)classifier).graph();
    } catch (Exception ex) {
    }
        }
        // copy full model for output
        SerializedObject so = new SerializedObject(classifier);
        fullClassifier = (Classifier) so.getObject();
      }
      
      switch (testMode) {
        case 3: // Test on training
        m_Log.statusMessage("Evaluating on training data...");
        eval = new Evaluation(inst, costMatrix);
        
        if (outputPredictionsText) {
    printPredictionsHeader(outBuff, inst, "training set");
        }


        for (int jj=0;jj<inst.numInstances();jj++) {
    processClassifierPrediction(inst.instance(jj), classifier,
              eval, predInstances, plotShape, 
              plotSize);
    
    if (outputPredictionsText) { 
      outBuff.append(predictionText(classifier, inst.instance(jj), jj+1));
    }
    if ((jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on training data. Processed "
              +jj+" instances...");
    }
        }
        if (outputPredictionsText) {
    outBuff.append("\n");
        } 
        outBuff.append("=== Evaluation on training set ===\n");
        break;


        case 1: // CV mode
        m_Log.statusMessage("Randomizing instances...");
        int rnd = 1;
        try {
    rnd = Integer.parseInt(m_RandomSeedText.getText().trim());
    // System.err.println("Using random seed "+rnd);
        } catch (Exception ex) {
    m_Log.logMessage("Trouble parsing random seed value");
    rnd = 1;
        }
        Random random = new Random(rnd);
        inst.randomize(random);
        if (inst.attribute(classIndex).isNominal()) {
    m_Log.statusMessage("Stratifying instances...");
    inst.stratify(numFolds);
        }
        eval = new Evaluation(inst, costMatrix);
      
        if (outputPredictionsText) {
    printPredictionsHeader(outBuff, inst, "test data");
        }


        // Make some splits and do a CV
        for (int fold = 0; fold < numFolds; fold++) {
    m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Eighth")
            + (fold + 1) + Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Nineth"));
    Instances train = inst.trainCV(numFolds, fold, random);
    eval.setPriors(train);
    m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Tenth")
            + (fold + 1) + Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Twelveth"));
    Classifier current = null;
    try {
      current = Classifier.makeCopy(template);
    } catch (Exception ex) {
      m_Log.logMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_LogMessage_Text_Fifth") + ex.getMessage());
    }
    current.buildClassifier(train);
    Instances test = inst.testCV(numFolds, fold);
    m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Eleventh")
            + (fold + 1) + Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Twelveth"));
    for (int jj=0;jj<test.numInstances();jj++) {
      processClassifierPrediction(test.instance(jj), current,
                eval, predInstances, plotShape,
                plotSize);
      if (outputPredictionsText) { 
        outBuff.append(predictionText(current, test.instance(jj), jj+1));
      }
    }
        }
        if (outputPredictionsText) {
    outBuff.append("\n");
        } 
        if (inst.attribute(classIndex).isNominal()) {
    outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_ThirtyThird"));
        } else {
    outBuff.append(Messages.getInstance().getString("ClassifierPanel_StartClassifier_OutBuffer_Text_ThirtyFourth"));
        }
        break;
    
        case 2: // Percent split
        if (!m_PreserveOrderBut.isSelected()) {
    m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Thirteenth"));
    try {
      rnd = Integer.parseInt(m_RandomSeedText.getText().trim());
    } catch (Exception ex) {
      m_Log.logMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Fourteenth"));
      rnd = 1;
    }
    inst.randomize(new Random(rnd));
        }
        int trainSize = (int) Math.round(inst.numInstances() * percent / 100);
        int testSize = inst.numInstances() - trainSize;
        Instances train = new Instances(inst, 0, trainSize);
        Instances test = new Instances(inst, trainSize, testSize);
        m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Fifteenth") + trainSize+ Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Sixteenth"));
        Classifier current = null;
        try {
    current = Classifier.makeCopy(template);
        } catch (Exception ex) {
    m_Log.logMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_LogMessage_Text_Sixth") + ex.getMessage());
        }
        current.buildClassifier(train);
        eval = new Evaluation(train, costMatrix);
        m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_StartClassifier_Log_StatusMessage_Text_Seventeenth"));
       
        if (outputPredictionsText) {
    printPredictionsHeader(outBuff, inst, Messages.getInstance().getString("ClassifierPanel_StartClassifier_PrintPredictionsHeader_Text_First"));

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    VisualizePanel temp_vp = null;
    String temp_grph = null;
    FastVector temp_preds = null;
    Attribute temp_classAtt = null;
    Classifier temp_classifier = null;
    Instances temp_trainHeader = null;
      
    if (o != null) { 
      for (int i = 0; i < o.size(); i++) {
  Object temp = o.elementAt(i);
  if (temp instanceof Classifier) {
    temp_classifier = (Classifier)temp;
  } else if (temp instanceof Instances) { // training header
    temp_trainHeader = (Instances)temp;
  } else if (temp instanceof VisualizePanel) { // normal errors
    temp_vp = (VisualizePanel)temp;
  } else if (temp instanceof String) { // graphable output
    temp_grph = (String)temp;
  } else if (temp instanceof FastVector) { // predictions
    temp_preds = (FastVector)temp;
  } else if (temp instanceof Attribute) { // class attribute
    temp_classAtt = (Attribute)temp;
  }
      }
    }


    final VisualizePanel vp = temp_vp;
    final String grph = temp_grph;
    final FastVector preds = temp_preds;
    final Attribute classAtt = temp_classAtt;
    final Classifier classifier = temp_classifier;
    final Instances trainHeader = temp_trainHeader;
    
    JMenuItem saveModel = new JMenuItem(Messages.getInstance().getString("ClassifierPanel_Visualize_SaveModel_JMenuItem_Text"));
    if (classifier != null) {
      saveModel.addActionListener(new ActionListener() {
    public void actionPerformed(ActionEvent e) {
      saveClassifier(selectedName, classifier, trainHeader);
    }
  });
    } else {
      saveModel.setEnabled(false);
    }
    resultListMenu.add(saveModel);


    JMenuItem reEvaluate =
      new JMenuItem(Messages.getInstance().getString("ClassifierPanel_Visualize_ReEvaluate_JMenuItem_Text"));
    if (classifier != null && m_TestLoader != null) {
      reEvaluate.addActionListener(new ActionListener() {
    public void actionPerformed(ActionEvent e) {
      reevaluateModel(selectedName, classifier, trainHeader);
    }
  });
    } else {
      reEvaluate.setEnabled(false);
    }
    resultListMenu.add(reEvaluate);
    
    resultListMenu.addSeparator();
    
    JMenuItem visErrors = new JMenuItem(Messages.getInstance().getString("ClassifierPanel_Visualize_VisErrors_JMenuItem_Text"));
    if (vp != null) {
      if ((vp.getXIndex() == 0) && (vp.getYIndex() == 1)) {
  try {
    vp.setXIndex(vp.getInstances().classIndex());  // class
    vp.setYIndex(vp.getInstances().classIndex() - 1);  // predicted class
  }
  catch (Exception e) {
    // ignored
  }
      }
      visErrors.addActionListener(new ActionListener() {
    public void actionPerformed(ActionEvent e) {
      visualizeClassifierErrors(vp);
    }
  });
    } else {
      visErrors.setEnabled(false);
    }
    resultListMenu.add(visErrors);


    JMenuItem visGrph = new JMenuItem(Messages.getInstance().getString("ClassifierPanel_Visualize_VisGrph_JMenuItem_Text_First"));
    if (grph != null) {
  if(((Drawable)temp_classifier).graphType()==Drawable.TREE) {
      visGrph.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
      String title;
      if (vp != null) title = vp.getName();
      else title = selectedName;
      visualizeTree(grph, title);
        }
    });
  }
  else if(((Drawable)temp_classifier).graphType()==Drawable.BayesNet) {
      visGrph.setText(Messages.getInstance().getString("ClassifierPanel_Visualize_VisGrph_JMenuItem_Text_Second"));
      visGrph.addActionListener(new ActionListener() {
        public void actionPerformed(ActionEvent e) {
      Thread th = new Thread() {
        public void run() {
        visualizeBayesNet(grph, selectedName);
        }
          };
      th.start();
        }
    });
  }
  else
      visGrph.setEnabled(false);
    } else {
      visGrph.setEnabled(false);
    }
    resultListMenu.add(visGrph);


    JMenuItem visMargin = new JMenuItem(Messages.getInstance().getString("ClassifierPanel_Visualize_VisMargin_JMenuItem_Text"));
    if (preds != null) {
      visMargin.addActionListener(new ActionListener() {
    public void actionPerformed(ActionEvent e) {
      try {
        MarginCurve tc = new MarginCurve();
        Instances result = tc.getCurve(preds);
        VisualizePanel vmc = new VisualizePanel();
        vmc.setName(result.relationName());
        vmc.setLog(m_Log);
        PlotData2D tempd = new PlotData2D(result);
        tempd.setPlotName(result.relationName());
        tempd.addInstanceNumberAttribute();
        vmc.addPlot(tempd);
        visualizeClassifierErrors(vmc);
      } catch (Exception ex) {
        ex.printStackTrace();
      }
    }
  });
    } else {
      visMargin.setEnabled(false);
    }
    resultListMenu.add(visMargin);


    JMenu visThreshold = new JMenu(Messages.getInstance().getString("ClassifierPanel_Visualize_VisThreshold_JMenu_Text"));
    if (preds != null && classAtt != null) {
      for (int i = 0; i < classAtt.numValues(); i++) {
  JMenuItem clv = new JMenuItem(classAtt.value(i));
  final int classValue = i;
  clv.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
        try {
    ThresholdCurve tc = new ThresholdCurve();
    Instances result = tc.getCurve(preds, classValue);
    //VisualizePanel vmc = new VisualizePanel();
    ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
    vmc.setROCString(Messages.getInstance().getString("ClassifierPanel_Visualize_VMC_SetROCString_Text_First") + 
         Utils.doubleToString(ThresholdCurve.getROCArea(result), 4) + Messages.getInstance().getString("ClassifierPanel_Visualize_VMC_SetROCString_Text_Second"));
    vmc.setLog(m_Log);
    vmc.setName(result.relationName()+Messages.getInstance().getString("ClassifierPanel_Visualize_VMC_SetName_Text_First") +
          classAtt.value(classValue) + Messages.getInstance().getString("ClassifierPanel_Visualize_VMC_SetName_Text_Second"));
    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);
    visualizeClassifierErrors(vmc);
        } catch (Exception ex) {
    ex.printStackTrace();
        }
        }
    });
    visThreshold.add(clv);
      }
    } else {
      visThreshold.setEnabled(false);
    }
    resultListMenu.add(visThreshold);
    
    JMenu visCostBenefit = new JMenu(Messages.getInstance().getString("ClassifierPanel_Visualize_VisCostBenefit_JMenu_Text"));
    if ((preds != null) && (classAtt != null) && (classAtt.isNominal())) {
      for (int i = 0; i < classAtt.numValues(); i++) {
        JMenuItem clv = new JMenuItem(classAtt.value(i));
        final int classValue = i;
        clv.addActionListener(new ActionListener() {
            public void actionPerformed(ActionEvent e) {
              try {
                ThresholdCurve tc = new ThresholdCurve();
                Instances result = tc.getCurve(preds, classValue);


                // Create a dummy class attribute with the chosen
                // class value as index 0 (if necessary).
                Attribute classAttToUse = classAtt;
                if (classValue != 0) {
                  FastVector newNames = new FastVector();
                  newNames.addElement(classAtt.value(classValue));
                  for (int k = 0; k < classAtt.numValues(); k++) {
                    if (k != classValue) {
                      newNames.addElement(classAtt.value(k));
                    }
                  }
                  classAttToUse = new Attribute(classAtt.name(), newNames);
                }
                
                CostBenefitAnalysis cbAnalysis = new CostBenefitAnalysis();
                
                PlotData2D tempd = new PlotData2D(result);
                tempd.setPlotName(result.relationName());
                tempd.m_alwaysDisplayPointsOfThisSize = 10;
                // 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);
                
                String windowTitle = "";
                if (classifier != null) {
                  String cname = classifier.getClass().getName();
                  if (cname.startsWith("weka.classifiers.")) {
                    windowTitle = "" + cname.substring("weka.classifiers.".length()) + " ";
                  }
                }
                windowTitle += Messages.getInstance().getString("ClassifierPanel_Visualize_WindowTitle_Text_First") + classAttToUse.value(0) + Messages.getInstance().getString("ClassifierPanel_Visualize_WindowTitle_Text_Second");

View Full Code Here

  protected void loadClassifier() {


    int returnVal = m_FileChooser.showOpenDialog(this);
    if (returnVal == JFileChooser.APPROVE_OPTION) {
      File selected = m_FileChooser.getSelectedFile();
      Classifier classifier = null;
      Instances trainHeader = null;


      m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_LoadClassifier_Log_StatusMessage_Text_First"));


      try {
  InputStream is = new FileInputStream(selected);
  if (selected.getName().endsWith(PMML_FILE_EXTENSION)) {
    PMMLModel model = PMMLFactory.getPMMLModel(is, m_Log);
    if (model instanceof PMMLClassifier) {
      classifier = (PMMLClassifier)model;
      /*trainHeader = 
        ((PMMLClassifier)classifier).getMiningSchema().getMiningSchemaAsInstances(); */
    } else {
      throw new Exception(Messages.getInstance().getString("ClassifierPanel_LoadClassifier_Exception_Text"));
    }
  } else {
  if (selected.getName().endsWith(".gz")) {
    is = new GZIPInputStream(is);
  }
  ObjectInputStream objectInputStream = new ObjectInputStream(is);
  classifier = (Classifier) objectInputStream.readObject();
  try { // see if we can load the header
    trainHeader = (Instances) objectInputStream.readObject();
  } catch (Exception e) {} // don't fuss if we can't
  objectInputStream.close();
  }
      } catch (Exception e) {
  
  JOptionPane.showMessageDialog(null, e, Messages.getInstance().getString("ClassifierPanel_LoadClassifier_JOptionPaneShowMessageDialog_Text"),
              JOptionPane.ERROR_MESSAGE);
      }  


      m_Log.statusMessage(Messages.getInstance().getString("ClassifierPanel_LoadClassifier_Log_StatusMessage_Text_Second"));
      
      if (classifier != null) {
  m_Log.logMessage(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_Log_LogMessage_Text_Fourth") + selected.getName()+ Messages.getInstance().getString("ClassifierPanel_SaveClassifier_Log_LogMessage_Text_Fifth"));
  String name = (new SimpleDateFormat("HH:mm:ss - ")).format(new Date());
  String cname = classifier.getClass().getName();
  if (cname.startsWith("weka.classifiers."))
    cname = cname.substring("weka.classifiers.".length());
  name += cname + Messages.getInstance().getString("ClassifierPanel_SaveClassifier_Name_Text_First") + selected.getName() + Messages.getInstance().getString("ClassifierPanel_SaveClassifier_Name_Text_Second");
  StringBuffer outBuff = new StringBuffer();


  outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_First"));
  outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_Second") + selected.getName() + "\n");
  outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_Fourth") + classifier.getClass().getName());
  if (classifier instanceof OptionHandler) {
    String [] o = ((OptionHandler) classifier).getOptions();
    outBuff.append(" " + Utils.joinOptions(o));
  }
  outBuff.append("\n");
  if (trainHeader != null) {
    outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_Fifth") + trainHeader.relationName() + '\n');
    outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_Seventh") + trainHeader.numAttributes() + '\n');
    if (trainHeader.numAttributes() < 100) {
      for (int i = 0; i < trainHeader.numAttributes(); i++) {
        outBuff.append("              " + trainHeader.attribute(i).name()
           + '\n');
      }
    } else {
      outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_Nineth"));
    }
  } else {
    outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_Tenth"));
  } 


  outBuff.append(Messages.getInstance().getString("ClassifierPanel_SaveClassifier_OutBuffer_Text_Eleventh"));
  outBuff.append(classifier.toString() + "\n");
  
  m_History.addResult(name, outBuff);
  m_History.setSingle(name);
  FastVector vv = new FastVector();
  vv.addElement(classifier);

View Full Code Here

    m_ClassifierEditor.setCapabilitiesFilter(filterClass);


    m_StartBut.setEnabled(true);
    // Check capabilities
    Capabilities currentFilter = m_ClassifierEditor.getCapabilitiesFilter();
    Classifier classifier = (Classifier) m_ClassifierEditor.getValue();
    Capabilities currentSchemeCapabilities =  null;
    if (classifier != null && currentFilter != null && 
        (classifier instanceof CapabilitiesHandler)) {
      currentSchemeCapabilities = ((CapabilitiesHandler)classifier).getCapabilities();

View Full Code Here

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