classifierpanel.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

	      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);

	      // Make some splits and do a CV
	      for (int fold = 0; fold < numFolds; fold++) {
		m_Log.statusMessage("Creating splits for fold "
				    + (fold + 1) + "...");
		Instances train = inst.trainCV(numFolds, fold, random);
		eval.setPriors(train);
		m_Log.statusMessage("Building model for fold "
				    + (fold + 1) + "...");
		classifier.buildClassifier(train);
		Instances test = inst.testCV(numFolds, fold);
		m_Log.statusMessage("Evaluating model for fold "
				    + (fold + 1) + "...");
		for (int jj=0;jj<test.numInstances();jj++) {
		  processClassifierPrediction(test.instance(jj), classifier,
					      eval, predictions,
					      predInstances, plotShape,
					      plotSize);
		}
	      }
	      if (inst.attribute(classIndex).isNominal()) {
		outBuff.append("=== Stratified cross-validation ===\n");
	      } else {
		outBuff.append("=== Cross-validation ===\n");
	      }
	      break;
		
	      case 2: // Percent split
	      m_Log.statusMessage("Randomizing instances...");
	      try {
		rnd = Integer.parseInt(m_RandomSeedText.getText().trim());
	      } catch (Exception ex) {
		m_Log.logMessage("Trouble parsing random seed value");
		rnd = 1;
	      }
	      inst.randomize(new Random(rnd));
	      int trainSize = 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("Building model on training split...");
	      classifier.buildClassifier(train);
	      eval = new Evaluation(train, costMatrix);
	      m_Log.statusMessage("Evaluating on test split...");
	     
	      for (int jj=0;jj<test.numInstances();jj++) {
		processClassifierPrediction(test.instance(jj), classifier,
					    eval, predictions,
					    predInstances, plotShape,
					    plotSize);

		if ((jj % 100) == 0) {
		  m_Log.statusMessage("Evaluating on test split. Processed "
				      +jj+" instances...");
		}
	      }
	      outBuff.append("=== Evaluation on test split ===\n");
	      break;
		
	      case 4: // Test on user split
	      m_Log.statusMessage("Evaluating on test data...");
	      eval = new Evaluation(inst, costMatrix);
	      
	      if (outputPredictionsText) {
		outBuff.append("=== Predictions on test set ===\n\n");
		outBuff.append(" inst#,    actual, predicted, error");
		if (inst.classAttribute().isNominal()) {
		  outBuff.append(", probability distribution");
		}
		outBuff.append("\n");
	      }

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

	      default:
	      throw new Exception("Test mode not implemented");
	    }
	    
	    if (outputSummary) {
	      outBuff.append(eval.toSummaryString(outputEntropy) + "\n");
	    }

	    if (inst.attribute(classIndex).isNominal()) {

	      if (outputPerClass) {
		outBuff.append(eval.toClassDetailsString() + "\n");
	      }

	      if (outputConfusion) {
		outBuff.append(eval.toMatrixString() + "\n");
	      }
	    }

	    m_History.updateResult(name);
	    m_Log.logMessage("Finished " + cname);
	    m_Log.statusMessage("OK");
	  } catch (Exception ex) {
	    ex.printStackTrace();
	    m_Log.logMessage(ex.getMessage());
	    JOptionPane.showMessageDialog(ClassifierPanel.this,
					  "Problem evaluating classifier:\n"
					  + ex.getMessage(),
					  "Evaluate classifier",
					  JOptionPane.ERROR_MESSAGE);
	    m_Log.statusMessage("Problem evaluating classifier");
	  } finally {
	    try {
	      if (predInstances != null && predInstances.numInstances() > 0) {
		if (predInstances.attribute(predInstances.classIndex())
		    .isNumeric()) {
		  postProcessPlotInfo(plotSize);
		}
		m_CurrentVis = new VisualizePanel();
		m_CurrentVis.setName(name+" ("+inst.relationName()+")");
		m_CurrentVis.setLog(m_Log);
		PlotData2D tempd = new PlotData2D(predInstances);
		tempd.setShapeSize(plotSize);
		tempd.setShapeType(plotShape);
		tempd.setPlotName(name+" ("+inst.relationName()+")");
		tempd.addInstanceNumberAttribute();
		
		m_CurrentVis.addPlot(tempd);
		m_CurrentVis.setColourIndex(predInstances.classIndex()+1);
		
		m_CurrentVis.setXIndex(m_visXIndex); 
		m_CurrentVis.setYIndex(m_visYIndex);
		
		m_CurrentVis.addActionListener(new ActionListener() {
		  public void actionPerformed(ActionEvent e) {
		    if (m_CurrentVis.getInstances().
			relationName().
			compareTo(m_Instances.relationName()) == 0) {
		      setXY_VisualizeIndexes(m_CurrentVis.getXIndex(), 
					     m_CurrentVis.getYIndex());
		    }
		  }
		  });
	    
		if (saveVis) {
		  FastVector vv = new FastVector();
		  if (outputModel) {
		    vv.addElement(fullClassifier);
		    Instances trainHeader = new Instances(m_Instances, 0);
		    trainHeader.setClassIndex(classIndex);
		    vv.addElement(trainHeader);
		  }
		  vv.addElement(m_CurrentVis);
		  if (grph != null) {
		    vv.addElement(grph);
		  }
		  if (predictions != null) {
		    vv.addElement(predictions);
		    vv.addElement(inst.classAttribute());
		  }
		  m_History.addObject(name, vv);
		} else if (outputModel) {
		  FastVector vv = new FastVector();
		  vv.addElement(fullClassifier);
		  Instances trainHeader = new Instances(m_Instances, 0);
		  trainHeader.setClassIndex(classIndex);
		  vv.addElement(trainHeader);
		  m_History.addObject(name, vv);
		}
	      }
	    } catch (Exception ex) {
	      ex.printStackTrace();
	    }
	    
	    if (isInterrupted()) {
	      m_Log.logMessage("Interrupted " + cname);
	      m_Log.statusMessage("Interrupted");
	    }

	    synchronized (this) {
	      m_StartBut.setEnabled(true);
	      m_StopBut.setEnabled(false);
	      m_RunThread = null;
	    }
	    if (m_Log instanceof TaskLogger) {
              ((TaskLogger)m_Log).taskFinished();
            }
          }
	}
      };
      m_RunThread.setPriority(Thread.MIN_PRIORITY);
      m_RunThread.start();
    }
  }

  protected String predictionText(Classifier classifier, Instance inst, int instNum) throws Exception {

    //> inst#   actual   predicted   error  probability distribution

    StringBuffer text = new StringBuffer();
    // inst #
    text.append(Utils.padLeft("" + instNum, 6) + " ");
    if (inst.classAttribute().isNominal()) {

      // actual
      if (inst.classIsMissing()) text.append(Utils.padLeft("?", 10) + " ");
      else text.append(Utils.padLeft("" + ((int) inst.classValue()+1) + ":"
				+ inst.stringValue(inst.classAttribute()), 10) + " ");

      // predicted
      double[] probdist = null;
      double pred;
      if (inst.classAttribute().isNominal()) {
	probdist = classifier.distributionForInstance(inst);
	pred = (double) Utils.maxIndex(probdist);
	if (probdist[(int) pred] <= 0.0) pred = Instance.missingValue();
      } else {
	pred = classifier.classifyInstance(inst);
      }
      text.append(Utils.padLeft((Instance.isMissingValue(pred) ? "?" :
				 (((int) pred+1) + ":"
				 + inst.classAttribute().value((int) pred))), 10) + " ");
      // error
      if (pred == inst.classValue()) text.append(Utils.padLeft(" ", 6) + " ");
      else text.append(Utils.padLeft("+", 6) + " ");

      // prob dist
      if (inst.classAttribute().type() == Attribute.NOMINAL) {
	for (int i=0; i<probdist.length; i++) {
	  if (i == (int) pred) text.append(" *");
	  else text.append("  ");
	  text.append(Utils.doubleToString(probdist[i], 5, 3));
	}
      }
    } else {

      // actual
      if (inst.classIsMissing()) text.append(Utils.padLeft("?", 10) + " ");
      else text.append(Utils.doubleToString(inst.classValue(), 10, 3) + " ");

      // predicted
      double pred = classifier.classifyInstance(inst);
      if (Instance.isMissingValue(pred)) text.append(Utils.padLeft("?", 10) + " ");
      else text.append(Utils.doubleToString(pred, 10, 3) + " ");

      // err
      if (!inst.classIsMissing() && !Instance.isMissingValue(pred))
	text.append(Utils.doubleToString(pred - inst.classValue(), 10, 3));
    }
    text.append("\n");
    return text.toString();
  }

  /**
   * Handles constructing a popup menu with visualization options.
   * @param name the name of the result history list entry clicked on by
   * the user
   * @param x the x coordinate for popping up the menu
   * @param y the y coordinate for popping up the menu
   */
  protected void visualize(String name, int x, int y) {
    final String selectedName = name;
    JPopupMenu resultListMenu = new JPopupMenu();
    
    JMenuItem visMainBuffer = new JMenuItem("View in main window");
    if (selectedName != null) {
      visMainBuffer.addActionListener(new ActionListener() {
	  public void actionPerformed(ActionEvent e) {
	    m_History.setSingle(selectedName);
	  }
	});
    } else {
      visMainBuffer.setEnabled(false);
    }
    resultListMenu.add(visMainBuffer);
    
    JMenuItem visSepBuffer = new JMenuItem("View in separate window");
    if (selectedName != null) {
      visSepBuffer.addActionListener(new ActionListener() {
	public void actionPerformed(ActionEvent e) {
	  m_History.openFrame(selectedName);
	}
      });
    } else {
      visSepBuffer.setEnabled(false);
    }
    resultListMenu.add(visSepBuffer);
    
    JMenuItem saveOutput = new JMenuItem("Save result buffer");
    if (selectedName != null) {
      saveOutput.addActionListener(new ActionListener() {
	  public void actionPerformed(ActionEvent e) {
	    saveBuffer(selectedName);
	  }
	});
    } else {
      saveOutput.setEnabled(false);
    }
    resultListMenu.add(saveOutput);
    
    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;