classifierpanel.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

	});
    } else {
      visTree.setEnabled(false);
    }
    resultListMenu.add(visTree);

    JMenuItem visMargin = new JMenuItem("Visualize margin curve");
    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("Visualize threshold curve");
    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();
		vmc.setLog(m_Log);
		vmc.setName(result.relationName()+". Class value "+
			    classAtt.value(classValue)+")");
		PlotData2D tempd = new PlotData2D(result);
		tempd.setPlotName(result.relationName());
		tempd.addInstanceNumberAttribute();
		vmc.addPlot(tempd);
		visualizeClassifierErrors(vmc);
	      } catch (Exception ex) {
		ex.printStackTrace();
	      }
	      }
	  });
	  visThreshold.add(clv);
      }
    } else {
      visThreshold.setEnabled(false);
    }
    resultListMenu.add(visThreshold);

    JMenu visCost = new JMenu("Visualize cost curve");
    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 {
		CostCurve cc = new CostCurve();
		Instances result = cc.getCurve(preds, classValue);
		VisualizePanel vmc = new VisualizePanel();
		vmc.setLog(m_Log);
		vmc.setName(result.relationName()+". Class value "+
			    classAtt.value(classValue)+")");
		PlotData2D tempd = new PlotData2D(result);
		tempd.m_displayAllPoints = true;
		tempd.setPlotName(result.relationName());
		boolean [] connectPoints = 
		  new boolean [result.numInstances()];
		for (int jj = 1; jj < connectPoints.length; jj+=2) {
		  connectPoints[jj] = true;
		}
		tempd.setConnectPoints(connectPoints);
		//		  tempd.addInstanceNumberAttribute();
		vmc.addPlot(tempd);
		visualizeClassifierErrors(vmc);
	      } catch (Exception ex) {
		ex.printStackTrace();
	      }
	    }
	  });
	visCost.add(clv);
      }
    } else {
      visCost.setEnabled(false);
    }
    resultListMenu.add(visCost);

    resultListMenu.show(m_History.getList(), x, y);
  }

  /**
   * Pops up a TreeVisualizer for the classifier from the currently
   * selected item in the results list
   * @param dottyString the description of the tree in dotty format
   * @param treeName the title to assign to the display
   */
  protected void visualizeTree(String dottyString, String treeName) {
    final javax.swing.JFrame jf = 
      new javax.swing.JFrame("Weka Classifier Tree Visualizer: "+treeName);
    jf.setSize(500,400);
    jf.getContentPane().setLayout(new BorderLayout());
    TreeVisualizer tv = new TreeVisualizer(null,
					   dottyString,
					   new PlaceNode2());
    jf.getContentPane().add(tv, BorderLayout.CENTER);
    jf.addWindowListener(new java.awt.event.WindowAdapter() {
	public void windowClosing(java.awt.event.WindowEvent e) {
	  jf.dispose();
	}
      });
    
    jf.setVisible(true);
    tv.fitToScreen();
  }

  /**
   * Pops up a VisualizePanel for visualizing the data and errors for 
   * the classifier from the currently selected item in the results list
   * @param sp the VisualizePanel to pop up.
   */
  protected void visualizeClassifierErrors(VisualizePanel sp) {
   
    if (sp != null) {
      String plotName = sp.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(sp, BorderLayout.CENTER);
	jf.addWindowListener(new java.awt.event.WindowAdapter() {
	  public void windowClosing(java.awt.event.WindowEvent e) {
	    jf.dispose();
	  }
	});

    jf.setVisible(true);
    }
  }

  /**
   * Save the currently selected classifier output to a file.
   * @param name the name of the buffer to save
   */
  protected void saveBuffer(String name) {
    StringBuffer sb = m_History.getNamedBuffer(name);
    if (sb != null) {
      if (m_SaveOut.save(sb)) {
	m_Log.logMessage("Save successful.");
      }
    }
  }
  

  /**
   * Stops the currently running classifier (if any).
   */
  protected void stopClassifier() {

    if (m_RunThread != null) {
      m_RunThread.interrupt();
      
      // This is deprecated (and theoretically the interrupt should do).
      m_RunThread.stop();
    }
  }

  /**
   * Saves the currently selected classifier
   */
  protected void saveClassifier(String name, Classifier classifier,
				Instances trainHeader) {

    File sFile = null;
    boolean saveOK = true;
 
    int returnVal = m_FileChooser.showSaveDialog(this);
    if (returnVal == JFileChooser.APPROVE_OPTION) {
      sFile = m_FileChooser.getSelectedFile();
      
      m_Log.statusMessage("Saving model to file...");
      
      try {
	OutputStream os = new FileOutputStream(sFile);
	if (sFile.getName().endsWith(".gz")) {
	  os = new GZIPOutputStream(os);
	}
	ObjectOutputStream objectOutputStream = new ObjectOutputStream(os);
	objectOutputStream.writeObject(classifier);
	if (trainHeader != null) objectOutputStream.writeObject(trainHeader);
	objectOutputStream.flush();
	objectOutputStream.close();
      } catch (Exception e) {
	
	JOptionPane.showMessageDialog(null, e, "Save Failed",
				      JOptionPane.ERROR_MESSAGE);
	saveOK = false;
      }
      if (saveOK)
	m_Log.logMessage("Saved model (" + name
			 + ") to file '" + sFile.getName() + "'");
      m_Log.statusMessage("OK");
    }
  }

  /**
   * Loads a classifier
   */
  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("Loading model from file...");

      try {
	InputStream is = new FileInputStream(selected);
	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, "Load Failed",
				      JOptionPane.ERROR_MESSAGE);
      }	

      m_Log.statusMessage("OK");
      
      if (classifier != null) {
	m_Log.logMessage("Loaded model from file '" + selected.getName()+ "'");
	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 + " from file '" + selected.getName() + "'";
	StringBuffer outBuff = new StringBuffer();

	outBuff.append("=== Model information ===\n\n");
	outBuff.append("Filename:     " + selected.getName() + "\n");
	outBuff.append("Scheme:       " + cname);
	if (classifier instanceof OptionHandler) {
	  String [] o = ((OptionHandler) classifier).getOptions();
	  outBuff.append(" " + Utils.joinOptions(o));
	}
	outBuff.append("\n");
	if (trainHeader != null) {
	  outBuff.append("Relation:     " + trainHeader.relationName() + '\n');
	  outBuff.append("Attributes:   " + 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("              [list of attributes omitted]\n");
	  }
	} else {
	  outBuff.append("\nTraining data unknown\n");
	} 

	outBuff.append("\n=== Classifier model ===\n\n");
	outBuff.append(classifier.toString() + "\n");
	
	m_History.addResult(name, outBuff);
	m_History.setSingle(name);
	FastVector vv = new FastVector();
	vv.addElement(classifier);
	if (trainHeader != null) vv.addElement(trainHeader);
	// allow visualization of graphable classifiers
	String grph = null;
	if (classifier instanceof Drawable) {
	  try {
	    grph = ((Drawable)classifier).graph();
	  } catch (Exception ex) {
	  }
	}
	if (grph != null) vv.addElement(grph);
	
	m_History.addObject(name, vv);
      }
    }
  }
  
  /**
   * Re-evaluates the named classifier with the current test set. Unpredictable
   * things will happen if the data set is not compatible with the classifier.
   *
   * @param name the name of the classifier entry
   * @param classifier the classifier to evaluate
   */
  protected void reevaluateModel(String name, Classifier classifier, Instances trainHeader) {

    StringBuffer outBuff = m_History.getNamedBuffer(name);
    Instances userTest = null;
    // additional vis info (either shape type or point size)
    FastVector plotShape = new FastVector();
    FastVector plotSize = new FastVector();
    Instances predInstances = null;
    
    // will hold the prediction objects if the class is nominal
    FastVector predictions = null;
    CostMatrix costMatrix = null;
    if (m_EvalWRTCostsBut.isSelected()) {
      costMatrix = new CostMatrix((CostMatrix) m_CostMatrixEditor
				  .getValue());
    }    
    boolean outputConfusion = m_OutputConfusionBut.isSelected();
    boolean outputPerClass = m_OutputPerClassBut.isSelected();
    boolean outputSummary = true;
    boolean outputEntropy = m_OutputEntropyBut.isSelected();
    boolean saveVis = m_StorePredictionsBut.isSelected();
    String grph = null;    
    
    try {

      if (m_TestInstances != null) {
	userTest = new Instances(m_TestInstancesCopy);
      }
      // Check the test instance compatibility
      if (userTest == null) {
	throw new Exception("No user test set has been opened");
      }
      if (trainHeader != null) {
	if (trainHeader.classIndex() > userTest.numAttributes()-1)
	  throw new Exception("Train and test set are not compatible");
	userTest.setClassIndex(trainHeader.classIndex());
	if (!trainHeader.equalHeaders(userTest)) {
	  throw new Exception("Train and test set are not compatible");
	}
      } else {
	userTest.setClassIndex(userTest.numAttributes()-1);
      }
      m_Log.statusMessage("Evaluating on test data...");
      m_Log.logMessage("Re-evaluating classifier (" + name + ") on test set");
      Evaluation eval = new Evaluation(userTest, costMatrix);
      
      // set up the structure of the plottable instances for 
      // visualization
      predInstances = setUpVisualizableInstances(userTest);
      predInstances.setClassIndex(userTest.classIndex()+1);
      
      if (userTest.classAttribute().isNominal() && 
	  classifier instanceof DistributionClassifier) {
	predictions = new FastVector();
      }
      
      for (int jj=0;jj<userTest.numInstances();jj++) {
	processClassifierPrediction(userTest.instance(jj), classifier,
				    eval, predictions,
				    predInstances, plotShape,
				    plotSize);
	if ((jj % 100) == 0) {
	  m_Log.statusMessage("Evaluating on test data. Processed "
			      +jj+" instances...");
	}
      }
      outBuff.append("\n=== Re-evaluation on test set ===\n\n");
      outBuff.append("User supplied test set\n");  
      outBuff.append("Relation:     " + userTest.relationName() + '\n');
      outBuff.append("Instances:    " + userTest.numInstances() + '\n');
      outBuff.append("Attributes:   " + userTest.numAttributes() + "\n\n");
      if (trainHeader == null)
	outBuff.append("NOTE - if test set is not compatible then results are "
		       + "unpredictable\n\n");