discretizefilter.java

:<<数据挖掘--实用机器学习技术及java实现>>一书的配套源程序

    // Entropy of the left and the right subsets
    entropyLeft = ContingencyTables.entropy(bestCounts[0]);
    entropyRight = ContingencyTables.entropy(bestCounts[1]);

    // Compute terms for MDL formula
    delta = Utils.log2(Math.pow(3, numClassesTotal) - 2) - 
      (((double) numClassesTotal * priorEntropy) - 
       (numClassesRight * entropyRight) - 
       (numClassesLeft * entropyLeft));

    // Check if split is to be accepted
    return (Utils.gr(gain, (Utils.log2(numCutPoints) + delta) /
		     (double)numInstances));
  }
    

  /** Selects cutpoints for sorted subset. */
  private double[] cutPointsForSubset(Instances instances, int attIndex, 
				      int first, int lastPlusOne) { 

    double[][] counts, bestCounts;
    double[] priorCounts, left, right, cutPoints;
    double currentCutPoint = -Double.MAX_VALUE, bestCutPoint = -1, 
      currentEntropy, bestEntropy, priorEntropy, gain;
    int bestIndex = -1, numInstances = 0, numCutPoints = 0;

    // Compute number of instances in set
    if ((lastPlusOne - first) < 2) {
      return null;
    }

    // Compute class counts.
    counts = new double[2][instances.numClasses()];
    for (int i = first; i < lastPlusOne; i++) {
      numInstances += instances.instance(i).weight();
      counts[1][(int)instances.instance(i).classValue()] +=
	instances.instance(i).weight();
    }

    // Save prior counts
    priorCounts = new double[instances.numClasses()];
    System.arraycopy(counts[1], 0, priorCounts, 0, 
		     instances.numClasses());

    // Entropy of the full set
    priorEntropy = ContingencyTables.entropy(priorCounts);
    bestEntropy = priorEntropy;
    
    // Find best entropy.
    bestCounts = new double[2][instances.numClasses()];
    for (int i = first; i < (lastPlusOne - 1); i++) {
      counts[0][(int)instances.instance(i).classValue()] +=
	instances.instance(i).weight();
      counts[1][(int)instances.instance(i).classValue()] -=
	instances.instance(i).weight();
      if (Utils.sm(instances.instance(i).value(attIndex), 
		   instances.instance(i + 1).value(attIndex))) {
	currentCutPoint = instances.instance(i).value(attIndex); //+ 
	//instances.instance(i + 1).value(attIndex)) / 2.0;
	currentEntropy = ContingencyTables.entropyConditionedOnRows(counts);
	if (Utils.sm(currentEntropy, bestEntropy)) {
	  bestCutPoint = currentCutPoint;
	  bestEntropy = currentEntropy;
	  bestIndex = i;
	  System.arraycopy(counts[0], 0, 
			   bestCounts[0], 0, instances.numClasses());
	  System.arraycopy(counts[1], 0, 
			   bestCounts[1], 0, instances.numClasses()); 
	}
	numCutPoints++;
      }
    }

    // Use worse encoding?
    if (!m_UseBetterEncoding) {
      numCutPoints = (lastPlusOne - first) - 1;
    }

    // Checks if gain is zero
    gain = priorEntropy - bestEntropy;
    if (Utils.eq(gain, 0)) {
      return null;
    }

    // Check if split is to be accepted
    if ((m_UseKononenko && KononenkosMDL(priorCounts, bestCounts,
					 numInstances, numCutPoints)) ||
	(!m_UseKononenko && FayyadAndIranisMDL(priorCounts, bestCounts,
					       numInstances, numCutPoints))) {
      
      // Select split points for the left and right subsets
      left = cutPointsForSubset(instances, attIndex, first, bestIndex + 1);
      right = cutPointsForSubset(instances, attIndex, 
				 bestIndex + 1, lastPlusOne);
      
      // Merge cutpoints and return them
      if ((left == null) && (right) == null) {
	cutPoints = new double[1];
	cutPoints[0] = bestCutPoint;
      } else if (right == null) {
	cutPoints = new double[left.length + 1];
	System.arraycopy(left, 0, cutPoints, 0, left.length);
	cutPoints[left.length] = bestCutPoint;
      } else if (left == null) {
	cutPoints = new double[1 + right.length];
	cutPoints[0] = bestCutPoint;
	System.arraycopy(right, 0, cutPoints, 1, right.length);
      } else {
	cutPoints =  new double[left.length + right.length + 1];
	cutPoints = new double[left.length + right.length + 1];
	System.arraycopy(left, 0, cutPoints, 0, left.length);
	cutPoints[left.length] = bestCutPoint;
	System.arraycopy(right, 0, cutPoints, left.length + 1, right.length);
      }
      
      return cutPoints;
    } else
      return null;
  }
 
  /**
   * Set cutpoints for a single attribute.
   *
   * @param index the index of the attribute to set cutpoints for
   */
  protected void calculateCutPointsByBinning(int index) {

    // Scan for max and min values
    double max = 0, min = 1, currentVal;
    Instance currentInstance;
    for(int i = 0; i < getInputFormat().numInstances(); i++) {
      currentInstance = getInputFormat().instance(i);
      if (!currentInstance.isMissing(index)) {
	currentVal = currentInstance.value(index);
	if (max < min) {
	  max = min = currentVal;
	}
	if (currentVal > max) {
	  max = currentVal;
	}
	if (currentVal < min) {
	  min = currentVal;
	}
      }
    }
    double binWidth = (max - min) / m_NumBins;
    double [] cutPoints = null;
    if ((m_NumBins > 1) && (binWidth > 0)) {
      cutPoints = new double [m_NumBins - 1];
      for(int i = 1; i < m_NumBins; i++) {
	cutPoints[i - 1] = min + binWidth * i;
      }
    }
    m_CutPoints[index] = cutPoints;
  }

  /**
   * Optimizes the number of bins using leave-one-out cross-validation.
   *
   * @param index the attribute index
   */
  protected void findNumBins(int index) {

    double min = Double.MAX_VALUE, max = -Double.MIN_VALUE, binWidth = 0, 
      entropy, bestEntropy = Double.MAX_VALUE, currentVal;
    double[] distribution;
    int bestNumBins  = 1;
    Instance currentInstance;

    // Find minimum and maximum
    for (int i = 0; i < getInputFormat().numInstances(); i++) {
      currentInstance = getInputFormat().instance(i);
      if (!currentInstance.isMissing(index)) {
	currentVal = currentInstance.value(index);
	if (currentVal > max) {
	  max = currentVal;
	}
	if (currentVal < min) {
	  min = currentVal;
	}
      }
    }

    // Find best number of bins
    for (int i = 0; i < m_NumBins; i++) {
      distribution = new double[i + 1];
      binWidth = (max - min) / (i + 1);

      // Compute distribution
      for (int j = 0; j < getInputFormat().numInstances(); j++) {
	currentInstance = getInputFormat().instance(j);
	if (!currentInstance.isMissing(index)) {
	  for (int k = 0; k < i + 1; k++) {
	    if (currentInstance.value(index) <= 
		(min + (((double)k + 1) * binWidth))) {
	      distribution[k] += currentInstance.weight();
	      break;
	    }
	  }
	}
      }

      // Compute cross-validated entropy
      entropy = 0;
      for (int k = 0; k < i + 1; k++) {
	if (distribution[k] < 2) {
	  entropy = Double.MAX_VALUE;
	  break;
	}
	entropy -= distribution[k] * Math.log((distribution[k] - 1) / 
					      binWidth);
      }

      // Best entropy so far?
      if (entropy < bestEntropy) {
	bestEntropy = entropy;
	bestNumBins = i + 1;
      }
    }

    // Compute cut points
    double [] cutPoints = null;
    if ((bestNumBins > 1) && (binWidth > 0)) {
      cutPoints = new double [bestNumBins - 1];
      for(int i = 1; i < bestNumBins; i++) {
	cutPoints[i - 1] = min + binWidth * i;
      }
    }
    m_CutPoints[index] = cutPoints;
  }

  /**
   * Set the output format. Takes the currently defined cutpoints and 
   * m_InputFormat and calls setOutputFormat(Instances) appropriately.
   */
  protected void setOutputFormat() {

    if (m_CutPoints == null) {
      setOutputFormat(null);
      return;
    }
    FastVector attributes = new FastVector(getInputFormat().numAttributes());
    int classIndex = getInputFormat().classIndex();
    for(int i = 0; i < getInputFormat().numAttributes(); i++) {
      if ((m_DiscretizeCols.isInRange(i)) 
	  && (getInputFormat().attribute(i).isNumeric())) {
	if (!m_MakeBinary) {
	  FastVector attribValues = new FastVector(1);
	  if (m_CutPoints[i] == null) {
	    attribValues.addElement("'All'");
	  } else {
	    for(int j = 0; j <= m_CutPoints[i].length; j++) {
	      if (j == 0) {
		attribValues.addElement("'(-inf-"
			+ Utils.doubleToString(m_CutPoints[i][j], 6) + "]'");
	      } else if (j == m_CutPoints[i].length) {
		attribValues.addElement("'("
			+ Utils.doubleToString(m_CutPoints[i][j - 1], 6) 
					+ "-inf)'");
	      } else {
		attribValues.addElement("'("
			+ Utils.doubleToString(m_CutPoints[i][j - 1], 6) + "-"
			+ Utils.doubleToString(m_CutPoints[i][j], 6) + "]'");
	      }
	    }
	  }
	  attributes.addElement(new Attribute(getInputFormat().
					      attribute(i).name(),
					      attribValues));
	} else {
	  if (m_CutPoints[i] == null) {
	    FastVector attribValues = new FastVector(1);
	    attribValues.addElement("'All'");
	    attributes.addElement(new Attribute(getInputFormat().
						attribute(i).name(),
						attribValues));
	  } else {
	    if (i < getInputFormat().classIndex()) {
	      classIndex += m_CutPoints[i].length - 1;
	    }
	    for(int j = 0; j < m_CutPoints[i].length; j++) {
	      FastVector attribValues = new FastVector(2);
	      attribValues.addElement("'(-inf-"
		      + Utils.doubleToString(m_CutPoints[i][j], 6) + "]'");
	      attribValues.addElement("'("
		      + Utils.doubleToString(m_CutPoints[i][j], 6) + "-inf)'");
	      attributes.addElement(new Attribute(getInputFormat().
						  attribute(i).name(),
						  attribValues));
	    }
	  }
	}
      } else {
	attributes.addElement(getInputFormat().attribute(i).copy());
      }
    }
    Instances outputFormat = 
      new Instances(getInputFormat().relationName(), attributes, 0);
    outputFormat.setClassIndex(classIndex);
    setOutputFormat(outputFormat);
  }

  /**
   * Convert a single instance over. The converted instance is added to 
   * the end of the output queue.
   *
   * @param instance the instance to convert
   */
  protected void convertInstance(Instance instance) {

    int index = 0;
    double [] vals = new double [outputFormatPeek().numAttributes()];
    // Copy and convert the values
    for(int i = 0; i < getInputFormat().numAttributes(); i++) {
      if (m_DiscretizeCols.isInRange(i) && 
	  getInputFormat().attribute(i).isNumeric()) {
	int j;
	double currentVal = instance.value(i);
	if (m_CutPoints[i] == null) {
	  if (instance.isMissing(i)) {
	    vals[index] = Instance.missingValue();
	  } else {
	    vals[index] = 0;
	  }
	  index++;
	} else {
	  if (!m_MakeBinary) {
	    if (instance.isMissing(i)) {
	      vals[index] = Instance.missingValue();
	    } else {
	      for (j = 0; j < m_CutPoints[i].length; j++) {
		if (currentVal <= m_CutPoints[i][j]) {
		  break;
		}
	      }
              vals[index] = j;
	    }
	    index++;
	  } else {
	    for (j = 0; j < m_CutPoints[i].length; j++) {
	      if (instance.isMissing(i)) {
                vals[index] = Instance.missingValue();
	      } else if (currentVal <= m_CutPoints[i][j]) {
                vals[index] = 0;
	      } else {
                vals[index] = 1;
	      }
	      index++;
	    }
	  }   
	}
      } else {
        vals[index] = instance.value(i);
	index++;
      }
    }
    
    Instance inst = null;
    if (instance instanceof SparseInstance) {
      inst = new SparseInstance(instance.weight(), vals);
    } else {
      inst = new Instance(instance.weight(), vals);
    }
    copyStringValues(inst, false, instance.dataset(), getInputStringIndex(),
                     getOutputFormat(), getOutputStringIndex());
    inst.setDataset(getOutputFormat());
    push(inst);
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain arguments to the filter: use -h for help
   */
  public static void main(String [] argv) {

    try {
      if (Utils.getFlag('b', argv)) {
 	Filter.batchFilterFile(new DiscretizeFilter(), argv);
      } else {
	Filter.filterFile(new DiscretizeFilter(), argv);
      }
    } catch (Exception ex) {
      System.out.println(ex.getMessage());
    }
  }
}