semisuppointactivecurvecvresultproducer.java

来自「wekaUT是 university texas austin 开发的基于wek」· Java 代码 · 共 1,185 行 · 第 1/3 页

	    if (m_IsTransductive) {
	      for (int i=0; i<test.numInstances(); i++) {
		unlabeledTrainSubsetWithLabels.add(test.instance(i));
	      }
	    }
	    
	    // Need to remove the class labels from the unlabeledTrainSubsetWithLabels data before training learner

	    Instances unlabeledTrainSubset = new Instances(unlabeledTrainSubsetWithLabels);
	    unlabeledTrainSubset.deleteClassAttribute();
		
	    Object [] seResults;
	    System.out.println("Starting index of test: " + maxTrainSize());
	    if (m_SplitEvaluator instanceof SemiSupClustererSplitEvaluator) {
	      seResults = ((SemiSupClustererSplitEvaluator) m_SplitEvaluator).getResult(labeledTrainSubset, unlabeledTrainSubset, train, test, maxTrainSize()); 
	    }
	    else {
	      throw new Exception("SplitEvaluator should be SemiSupClustererSplitEvaluator - SemiSupClassifierSplitEvaluator not yet implemented");
	    }
	    
	    Object [] results = new Object [seResults.length + 1];
	    results[0] = getTimestamp();
	    System.arraycopy(seResults, 0, results, 1,
			     seResults.length);
	    if (m_debugOutput) {
	      String resultName = (""+run+"."+(fold+1)+"."+ m_CurrentSize + "." 
				   + Utils.backQuoteChars(runInstances.relationName())
				   +"."
				   +m_SplitEvaluator.toString()).replace(' ','_');
	      resultName = Utils.removeSubstring(resultName, 
						 "weka.clusterers.");
	      resultName = Utils.removeSubstring(resultName, 
						 "weka.filters.");
	      resultName = Utils.removeSubstring(resultName, 
						 "weka.attributeSelection.");
	      m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
	    }
	    m_ResultListener.acceptResult(this, key, results);
	  } catch (Exception ex) {
	    // Save the train and test datasets for debugging purposes?
	    throw ex;
	  }
	}
	if (m_PlotPoints != null) {
	  pointNum ++;
	  int oldCurrentSize = m_CurrentSize;
	  m_CurrentSize = plotPoint(pointNum);
	  if (m_DoActive && m_CurrentSize<maxTrainSize()) {
	    ActiveLearningClusterer clusterer = (ActiveLearningClusterer) ((SemiSupClustererSplitEvaluator)m_SplitEvaluator).getClusterer();
	    int[] instancesForActiveLearning = clusterer.bestInstancesForActiveLearning(m_CurrentSize - oldCurrentSize);
	    train = reorganizeTrainForActiveLearning(train, oldCurrentSize, instancesForActiveLearning);
	  }
	}
	else {
	  int oldCurrentSize = m_CurrentSize;
	  m_CurrentSize += m_StepSize;
	  if (m_DoActive && m_CurrentSize<maxTrainSize()) {
	    ActiveLearningClusterer clusterer = (ActiveLearningClusterer) ((SemiSupClustererSplitEvaluator)m_SplitEvaluator).getClusterer();
	    int[] instancesForActiveLearning = clusterer.bestInstancesForActiveLearning(m_StepSize);
	    train = reorganizeTrainForActiveLearning(train, oldCurrentSize, instancesForActiveLearning);
	  }
	}
      }
    }
  }
  
  public Instances reorganizeTrainForActiveLearning(Instances train, int currentSize, int[] instancesForActiveLearning) {
    Instances newTrain = new Instances (train,0);
    int numTrain = train.numInstances();
    int[] marks = new int[numTrain];
    //    System.out.println("Number of training instances:" + numTrain);
    
    for (int i=0; i<currentSize; i++) {
      newTrain.add(train.instance(i));
      System.out.println("Adding index: " + i + " with class " + train.instance(i).classValue());
    }
    if (currentSize < numTrain) {
      for (int i=0; i<instancesForActiveLearning.length; i++) {
	int index = instancesForActiveLearning[i];
	marks[index] = 1;
	System.out.println("Now adding index: " + index + " with class " + train.instance(index).classValue());
	newTrain.add(train.instance(index));
      }
      for (int i=currentSize; i<numTrain; i++) {
	if (marks[i] == 0) {
	  newTrain.add(train.instance(i));
	  System.out.println("Remaining index: " + i + " with class " + train.instance(i).classValue());
	}
      }
    }
    return newTrain;
  }

  /** Determines if the points specified are fractions of the total number of examples */
  protected boolean setIsFraction(){
    if (m_PlotPoints != null){
      if(!isInteger(m_PlotPoints[0]))//if the first point is not an integer
	m_IsFraction = true;
      else
	m_IsFraction = false;
    }
    return m_IsFraction;
  }
  
  /** Return the number of training examples for the ith point on the
   * curve for plotPoints as specified.
   */
  protected int plotPoint(int i) {
    // If i beyond number of given plot points return a value greater than maximum training size
    if (i >= m_PlotPoints.length)
      return maxTrainSize() + 1;
    double point = m_PlotPoints[i];
    // If plot point is an integer (other than a non-initial 1)
    // treat it as a specific number of examples
    if (isInteger(point) && !(Utils.eq(point, 1.0) && i!=0))
      return (int)point;
    else
      // Otherwise, treat it as a percentage of the full set
      return (int)Math.round(point * maxTrainSize());
  }
  
  /** Return true if the given double represents an integer value */
  protected static boolean isInteger(double val) {
    return Utils.eq(Math.floor(val), Math.ceil(val));
  }
  
  /**
   * Gets the names of each of the columns produced for a single run.
   * This method should really be static.
   *
   * @return an array containing the name of each column
   */
  public String [] getKeyNames() {

    String [] keyNames = m_SplitEvaluator.getKeyNames();
    // Add in the names of our extra key fields
    int numExtraKeys;
    if(m_IsFraction)
      numExtraKeys = 5;
    else 
      numExtraKeys = 4;
    String [] newKeyNames = new String [keyNames.length + numExtraKeys];
    newKeyNames[0] = DATASET_FIELD_NAME;
    newKeyNames[1] = RUN_FIELD_NAME;
    newKeyNames[2] = FOLD_FIELD_NAME;
    newKeyNames[3] = STEP_FIELD_NAME;
    if(m_IsFraction) newKeyNames[4] = FRACTION_FIELD_NAME;
    System.arraycopy(keyNames, 0, newKeyNames, numExtraKeys, keyNames.length);
    return newKeyNames;
  }

  /**
   * Gets the data types of each of the columns produced for a single run.
   * This method should really be static.
   *
   * @return an array containing objects of the type of each column. The 
   * objects should be Strings, or Doubles.
   */
  public Object [] getKeyTypes() {
    Object [] keyTypes = m_SplitEvaluator.getKeyTypes();
    int numExtraKeys;
    if(m_IsFraction)
      numExtraKeys = 5;
    else 
      numExtraKeys = 4;
    // Add in the types of our extra fields
    Object [] newKeyTypes = new String [keyTypes.length + numExtraKeys];
    newKeyTypes[0] = new String();
    newKeyTypes[1] = new String();
    newKeyTypes[2] = new String();
    newKeyTypes[3] = new String();
    if(m_IsFraction) newKeyTypes[4] = new String();
    System.arraycopy(keyTypes, 0, newKeyTypes, numExtraKeys, keyTypes.length);
    return newKeyTypes;
  }

  /**
   * Gets the names of each of the columns produced for a single run.
   * This method should really be static.
   *
   * @return an array containing the name of each column
   */
  public String [] getResultNames() {

    String [] resultNames = m_SplitEvaluator.getResultNames();
    // Add in the names of our extra Result fields
    String [] newResultNames = new String [resultNames.length + 1];
    newResultNames[0] = TIMESTAMP_FIELD_NAME;
    System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length);
    return newResultNames;
  }

  /**
   * Gets the data types of each of the columns produced for a single run.
   * This method should really be static.
   *
   * @return an array containing objects of the type of each column. The 
   * objects should be Strings, or Doubles.
   */
  public Object [] getResultTypes() {

    Object [] resultTypes = m_SplitEvaluator.getResultTypes();
    // Add in the types of our extra Result fields
    Object [] newResultTypes = new Object [resultTypes.length + 1];
    newResultTypes[0] = new Double(0);
    System.arraycopy(resultTypes, 0, newResultTypes, 1, resultTypes.length);
    return newResultTypes;
  }

  /**
   * Gets a description of the internal settings of the result
   * producer, sufficient for distinguishing a ResultProducer
   * instance from another with different settings (ignoring
   * those settings set through this interface). For example,
   * a cross-validation ResultProducer may have a setting for the
   * number of folds. For a given state, the results produced should
   * be compatible. Typically if a ResultProducer is an OptionHandler,
   * this string will represent the command line arguments required
   * to set the ResultProducer to that state.
   *
   * @return the description of the ResultProducer state, or null
   * if no state is defined
   */
  public String getCompatibilityState() {

    String result = "-X " + m_NumFolds + " -S " + getStepSize() + 
	" -L " + getLowerSize() + " -U " + getUpperSize() + " ";
    if (m_SplitEvaluator == null) {
      result += "<null SplitEvaluator>";
    } else {
      result += "-W " + m_SplitEvaluator.getClass().getName();
    }
    return result + " --";
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String outputFileTipText() {
    return "Set the destination for saving raw output. If the rawOutput "
      +"option is selected, then output from the splitEvaluator for "
      +"individual folds is saved. If the destination is a directory, "
      +"then each output is saved to an individual gzip file; if the "
      +"destination is a file, then each output is saved as an entry "
      +"in a zip file.";
  }

  /**
   * Get the value of OutputFile.
   *
   * @return Value of OutputFile.
   */
  public File getOutputFile() {
    
    return m_OutputFile;
  }
  
  /**
   * Set the value of OutputFile.
   *
   * @param newOutputFile Value to assign to OutputFile.
   */
  public void setOutputFile(File newOutputFile) {
    
    m_OutputFile = newOutputFile;
  }  

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numFoldsTipText() {
    return "Number of folds to use in cross validation.";
  }

  /**
   * Get the value of NumFolds.
   *
   * @return Value of NumFolds.
   */
  public int getNumFolds() {
    
    return m_NumFolds;
  }

  
  /**
   * Set the value of NumFolds.
   *
   * @param newNumFolds Value to assign to NumFolds.
   */
  public void setNumFolds(int newNumFolds) {
    
    m_NumFolds = newNumFolds;
  }


  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String isTransductiveTipText() {
    return "Whether evaluation is transductive or not.";
  }

  /**
   * Get the value of IsTransductive.
   *
   * @return Value of IsTransductive.
   */
  public boolean getIsTransductive() {
    
    return m_IsTransductive;
  }

  
  /**
   * Set the value of IsTransductive.
   *
   * @param flag Value to assign to IsTransductive.
   */
  public void setIsTransductive(boolean flag) {
    
    m_IsTransductive = flag;
  }


  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String DoActiveTipText() {
    return "Whether active learning is performed or not.";
  }

  /**
   * Get the value of m_DoActive.
   *
   * @return Value of m_DoActive.
   */
  public boolean getDoActive() {
    
    return m_DoActive;
  }

  
  /**
   * Set the value of m_DoActive.
   *
   * @param flag Value to assign to m_DoActive.
   */
  public void setDoActive(boolean flag) {
    
    m_DoActive = flag;
  }


  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String lowerSizeTipText() {
    return "Set the minimum number of instances in a training set. Setting zero "
            + "here will actually use <stepSize> number of instances at the first "
            + "step (since performance at zero instances is predictable)";
  }

  /**
   * Get the value of LowerSize.
   *
   * @return Value of LowerSize.
   */
  public int getLowerSize() {
    
    return m_LowerSize;
  }
  
  /**
   * Set the value of LowerSize.
   *
   * @param newLowerSize Value to assign to
   * LowerSize.
   */
  public void setLowerSize(int newLowerSize) {
    
    m_LowerSize = newLowerSize;
  }

  /**