classifierpanel.java

来自「Weka」· Java 代码 · 共 1,867 行 · 第 1/5 页

   * Sets the Logger to receive informational messages
   *
   * @param newLog the Logger that will now get info messages
   */
  public void setLog(Logger newLog) {

    m_Log = newLog;
  }

  /**
   * Tells the panel to use a new set of instances.
   *
   * @param inst a set of Instances
   */
  public void setInstances(Instances inst) {
    m_Instances = inst;

    String [] attribNames = new String [m_Instances.numAttributes()];
    for (int i = 0; i < attribNames.length; i++) {
      String type = "";
      switch (m_Instances.attribute(i).type()) {
      case Attribute.NOMINAL:
	type = "(Nom) ";
	break;
      case Attribute.NUMERIC:
	type = "(Num) ";
	break;
      case Attribute.STRING:
	type = "(Str) ";
	break;
      case Attribute.DATE:
	type = "(Dat) ";
	break;
      case Attribute.RELATIONAL:
	type = "(Rel) ";
	break;
      default:
	type = "(???) ";
      }
      attribNames[i] = type + m_Instances.attribute(i).name();
    }
    m_ClassCombo.setModel(new DefaultComboBoxModel(attribNames));
    if (attribNames.length > 0) {
      if (inst.classIndex() == -1)
	m_ClassCombo.setSelectedIndex(attribNames.length - 1);
      else
	m_ClassCombo.setSelectedIndex(inst.classIndex());
      m_ClassCombo.setEnabled(true);
      m_StartBut.setEnabled(m_RunThread == null);
      m_StopBut.setEnabled(m_RunThread != null);
    } else {
      m_StartBut.setEnabled(false);
      m_StopBut.setEnabled(false);
    }
  }

  /**
   * Sets the user test set. Information about the current test set
   * is displayed in an InstanceSummaryPanel and the user is given the
   * ability to load another set from a file or url.
   *
   */
  protected void setTestSet() {

    if (m_SetTestFrame == null) {
      final SetInstancesPanel sp = new SetInstancesPanel();

      if (m_TestLoader != null) {
        try {
          if (m_TestLoader.getStructure() != null)
            sp.setInstances(m_TestLoader.getStructure());
        } catch (Exception ex) {
          ex.printStackTrace();
        }
      }
      sp.addPropertyChangeListener(new PropertyChangeListener() {
	public void propertyChange(PropertyChangeEvent e) {
	  m_TestLoader = sp.getLoader();
	}
      });
      // Add propertychangelistener to update m_TestLoader whenever
      // it changes in the settestframe
      m_SetTestFrame = new JFrame("Test Instances");
      sp.setParentFrame(m_SetTestFrame);   // enable Close-Button
      m_SetTestFrame.getContentPane().setLayout(new BorderLayout());
      m_SetTestFrame.getContentPane().add(sp, BorderLayout.CENTER);
      m_SetTestFrame.pack();
    }
    m_SetTestFrame.setVisible(true);
  }

  /**
   * Process a classifier's prediction for an instance and update a
   * set of plotting instances and additional plotting info. plotInfo
   * for nominal class datasets holds shape types (actual data points have
   * automatic shape type assignment; classifier error data points have
   * box shape type). For numeric class datasets, the actual data points
   * are stored in plotInstances and plotInfo stores the error (which is
   * later converted to shape size values)
   * @param toPredict the actual data point
   * @param classifier the classifier
   * @param eval the evaluation object to use for evaluating the classifier on
   * the instance to predict
   * @param plotInstances a set of plottable instances
   * @param plotShape additional plotting information (shape)
   * @param plotSize additional plotting information (size)
   */
  public static void processClassifierPrediction(Instance toPredict,
                                           Classifier classifier,
					   Evaluation eval,
					   Instances plotInstances,
					   FastVector plotShape,
					   FastVector plotSize) {
    try {
      double pred = eval.evaluateModelOnceAndRecordPrediction(classifier, 
							      toPredict);

      if (plotInstances != null) {
        double [] values = new double[plotInstances.numAttributes()];
        for (int i = 0; i < plotInstances.numAttributes(); i++) {
          if (i < toPredict.classIndex()) {
            values[i] = toPredict.value(i);
          } else if (i == toPredict.classIndex()) {
            values[i] = pred;
            values[i+1] = toPredict.value(i);
            /* // if the class value of the instances to predict is missing then
            // set it to the predicted value
            if (toPredict.isMissing(i)) {
	    values[i+1] = pred;
	    } */
            i++;
          } else {
            values[i] = toPredict.value(i-1);
          }
        }

        plotInstances.add(new Instance(1.0, values));
        if (toPredict.classAttribute().isNominal()) {
          if (toPredict.isMissing(toPredict.classIndex()) 
              || Instance.isMissingValue(pred)) {
            plotShape.addElement(new Integer(Plot2D.MISSING_SHAPE));
          } else if (pred != toPredict.classValue()) {
            // set to default error point shape
            plotShape.addElement(new Integer(Plot2D.ERROR_SHAPE));
          } else {
            // otherwise set to constant (automatically assigned) point shape
            plotShape.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
          }
          plotSize.addElement(new Integer(Plot2D.DEFAULT_SHAPE_SIZE));
        } else {
          // store the error (to be converted to a point size later)
          Double errd = null;
          if (!toPredict.isMissing(toPredict.classIndex()) && 
              !Instance.isMissingValue(pred)) {
            errd = new Double(pred - toPredict.classValue());
            plotShape.addElement(new Integer(Plot2D.CONST_AUTOMATIC_SHAPE));
          } else {
            // missing shape if actual class not present or prediction is missing
            plotShape.addElement(new Integer(Plot2D.MISSING_SHAPE));
          }
          plotSize.addElement(errd);
        }
      }
    } catch (Exception ex) {
      ex.printStackTrace();
    }
  }

  /**
   * Post processes numeric class errors into shape sizes for plotting
   * in the visualize panel
   * @param plotSize a FastVector of numeric class errors
   */
  private void postProcessPlotInfo(FastVector plotSize) {
    int maxpSize = 20;
    double maxErr = Double.NEGATIVE_INFINITY;
    double minErr = Double.POSITIVE_INFINITY;
    double err;
    
    for (int i = 0; i < plotSize.size(); i++) {
      Double errd = (Double)plotSize.elementAt(i);
      if (errd != null) {
	err = Math.abs(errd.doubleValue());
        if (err < minErr) {
	  minErr = err;
	}
	if (err > maxErr) {
	  maxErr = err;
	}
      }
    }
    
    for (int i = 0; i < plotSize.size(); i++) {
      Double errd = (Double)plotSize.elementAt(i);
      if (errd != null) {
	err = Math.abs(errd.doubleValue());
	if (maxErr - minErr > 0) {
	  double temp = (((err - minErr) / (maxErr - minErr)) 
			 * maxpSize);
	  plotSize.setElementAt(new Integer((int)temp), i);
	} else {
	  plotSize.setElementAt(new Integer(1), i);
	}
      } else {
	plotSize.setElementAt(new Integer(1), i);
      }
    }
  }

  /**
   * Sets up the structure for the visualizable instances. This dataset
   * contains the original attributes plus the classifier's predictions
   * for the class as an attribute called "predicted+WhateverTheClassIsCalled".
   * @param trainInstances the instances that the classifier is trained on
   * @return a new set of instances containing one more attribute (predicted
   * class) than the trainInstances
   */
  public static Instances setUpVisualizableInstances(Instances trainInstances) {
    FastVector hv = new FastVector();
    Attribute predictedClass;

    Attribute classAt = trainInstances.attribute(trainInstances.classIndex());
    if (classAt.isNominal()) {
      FastVector attVals = new FastVector();
      for (int i = 0; i < classAt.numValues(); i++) {
	attVals.addElement(classAt.value(i));
      }
      predictedClass = new Attribute("predicted"+classAt.name(), attVals);
    } else {
      predictedClass = new Attribute("predicted"+classAt.name());
    }

    for (int i = 0; i < trainInstances.numAttributes(); i++) {
      if (i == trainInstances.classIndex()) {
	hv.addElement(predictedClass);
      }
      hv.addElement(trainInstances.attribute(i).copy());
    }
    return new Instances(trainInstances.relationName()+"_predicted", hv, 
			 trainInstances.numInstances());
  }

  /**
   * outputs the header for the predictions on the data
   * 
   * @param outBuff	the buffer to add the output to
   * @param inst	the data header
   * @param title	the title to print
   */
  protected void printPredictionsHeader(StringBuffer outBuff, Instances inst, String title) {
    outBuff.append("=== Predictions on " + title + " ===\n\n");
    outBuff.append(" inst#,    actual, predicted, error");
    if (inst.classAttribute().isNominal()) {
      outBuff.append(", probability distribution");
    }
    if (m_OutputAdditionalAttributesRange != null) {
      outBuff.append(" (");
      boolean first = true;
      for (int i = 0; i < inst.numAttributes() - 1; i++) {
	if (m_OutputAdditionalAttributesRange.isInRange(i)) {
	  if (!first)
	    outBuff.append(",");
	  else
	    first = false;
	  outBuff.append(inst.attribute(i).name());
	}
      }
      outBuff.append(")");
    }
    outBuff.append("\n");
  }
  
  /**
   * Starts running the currently configured classifier with the current
   * settings. This is run in a separate thread, and will only start if
   * there is no classifier already running. The classifier output is sent
   * to the results history panel.
   */
  protected void startClassifier() {

    if (m_RunThread == null) {
      synchronized (this) {
	m_StartBut.setEnabled(false);
	m_StopBut.setEnabled(true);
      }
      m_RunThread = new Thread() {
	public void run() {
	  // Copy the current state of things
	  m_Log.statusMessage("Setting up...");
	  CostMatrix costMatrix = null;
	  Instances inst = new Instances(m_Instances);
	  DataSource source = null;
          Instances userTestStructure = null;
	  // additional vis info (either shape type or point size)
	  FastVector plotShape = new FastVector();
	  FastVector plotSize = new FastVector();
	  Instances predInstances = null;
	 
	  // for timing
	  long trainTimeStart = 0, trainTimeElapsed = 0;

          try {
            if (m_TestLoader != null && m_TestLoader.getStructure() != null) {
              m_TestLoader.reset();
              source = new DataSource(m_TestLoader);
              userTestStructure = source.getStructure();
            }
          } catch (Exception ex) {
            ex.printStackTrace();
          }
	  if (m_EvalWRTCostsBut.isSelected()) {
	    costMatrix = new CostMatrix((CostMatrix) m_CostMatrixEditor
					.getValue());
	  }
	  boolean outputModel = m_OutputModelBut.isSelected();
	  boolean outputConfusion = m_OutputConfusionBut.isSelected();
	  boolean outputPerClass = m_OutputPerClassBut.isSelected();
	  boolean outputSummary = true;
          boolean outputEntropy = m_OutputEntropyBut.isSelected();
	  boolean saveVis = m_StorePredictionsBut.isSelected();
	  boolean outputPredictionsText = m_OutputPredictionsTextBut.isSelected();
	  if (m_OutputAdditionalAttributesText.getText().equals("")) {
	    m_OutputAdditionalAttributesRange = null;
	  }
	  else {
	    m_OutputAdditionalAttributesRange = new Range(m_OutputAdditionalAttributesText.getText());
	    m_OutputAdditionalAttributesRange.setUpper(inst.numAttributes() - 1);
	  }

	  String grph = null;

	  int testMode = 0;
	  int numFolds = 10, 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("Problem copying classifier: " + 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("Number of folds must be greater than 1");
	      }
	    } else if (m_PercentBut.isSelected()) {
	      testMode = 2;
	      percent = Integer.parseInt(m_PercentText.getText());
	      if ((percent <= 0) || (percent >= 100)) {
		throw new Exception("Percentage must be between 0 and 100");
	      }
	    } else if (m_TrainBut.isSelected()) {
	      testMode = 3;
	    } else if (m_TestSplitBut.isSelected()) {
	      testMode = 4;
	      // Check the test instance compatibility
	      if (source == null) {