classifierpanel.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

	public void actionPerformed(ActionEvent e) {
	  m_History.removeResult(selectedName);
	}
      });
    } else {
      deleteOutput.setEnabled(false);
    }
    resultListMenu.add(deleteOutput);

    resultListMenu.addSeparator();
    
    JMenuItem loadModel = new JMenuItem("Load model");
    loadModel.addActionListener(new ActionListener() {
	public void actionPerformed(ActionEvent e) {
	  loadClassifier();
	}
      });
    resultListMenu.add(loadModel);

    FastVector o = null;
    if (selectedName != null) {
      o = (FastVector)m_History.getNamedObject(selectedName);
    }

    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("Save model");
    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("Re-evaluate model on current test set");
    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("Visualize classifier errors");
    if (vp != null) {
      visErrors.addActionListener(new ActionListener() {
	  public void actionPerformed(ActionEvent e) {
	    visualizeClassifierErrors(vp);
	  }
	});
    } else {
      visErrors.setEnabled(false);
    }
    resultListMenu.add(visErrors);

    JMenuItem visGrph = new JMenuItem("Visualize tree");
    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("Visualize graph");
	    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("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();
		ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
		vmc.setROCString("(Area under ROC = " + 
				 Utils.doubleToString(ThresholdCurve.getROCArea(result), 4) + ")");
		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);
    
    JMenu visPlugins = new JMenu("Plugins");
    Vector pluginsVector = ClassDiscovery.find(VisualizePlugin.class, "weka.gui.visualize.plugins");
    boolean availablePlugins = false;
    for (int i=0; i<pluginsVector.size(); i++) {
      String className = (String)(pluginsVector.elementAt(i));
      try {
        if (className.matches(".*\\.VisualizePlugin"))
          continue;
        VisualizePlugin plugin = (VisualizePlugin)(Class.forName(className).newInstance());
        if (plugin==null)
          continue;
        availablePlugins = true;
        JMenuItem pluginMenuItem = plugin.getVisualizeMenuItem(preds, classAtt);
        Version version = new Version();
        if (pluginMenuItem != null) {
          if (version.compareTo(plugin.getMinVersion()) < 0)
            pluginMenuItem.setText(pluginMenuItem.getText() + " (weka outdated)");
          if (version.compareTo(plugin.getMaxVersion()) >= 0)
            pluginMenuItem.setText(pluginMenuItem.getText() + " (plugin outdated)");
          visPlugins.add(pluginMenuItem);
        }
      }
      catch (ClassNotFoundException cnfe) {
        //System.out.println("Visualize plugin ClassNotFoundException " + cnfe.getMessage());
      }
      catch (InstantiationException ie) {
        //System.out.println("Visualize plugin InstantiationException " + ie.getMessage());
      }
      catch (IllegalAccessException iae) {
        //System.out.println("Visualize plugin IllegalAccessException " + iae.getMessage());
      }
    }
    if (availablePlugins)
      resultListMenu.add(visPlugins);

    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 GraphVisualizer for the BayesNet classifier from the currently
   * selected item in the results list
   * 
   * @param XMLBIF the description of the graph in XMLBIF ver. 0.3
   * @param graphName the name of the graph
   */
  protected void visualizeBayesNet(String XMLBIF, String graphName) {
    final javax.swing.JFrame jf = 
      new javax.swing.JFrame("Weka Classifier Graph Visualizer: "+graphName);
    jf.setSize(500,400);
    jf.getContentPane().setLayout(new BorderLayout());
    GraphVisualizer gv = new GraphVisualizer();
    try { gv.readBIF(XMLBIF);
    }
    catch(BIFFormatException be) { System.err.println("unable to visualize BayesNet"); be.printStackTrace(); }
    gv.layoutGraph();

    jf.getContentPane().add(gv, BorderLayout.CENTER);
    jf.addWindowListener(new java.awt.event.WindowAdapter() {
	public void windowClosing(java.awt.event.WindowEvent e) {
	  jf.dispose();
	}
      });
    
    jf.setVisible(true);
  }


  /**
   * 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(600,400);
	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
   * 
   * @param name the name of the run
   * @param classifier the classifier to save
   * @param trainHeader the header of the training instances
   */
  protected void saveClassifier(String name, Classifier classifier,
				Instances trainHeader) {

    File sFile = null;
    boolean saveOK = true;
 
    int returnVal = m_Fil