vfi.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

   * @param instances set of instances serving as training data 
   * @exception Exception if the classifier has not been generated successfully
   */
  public void buildClassifier(Instances instances) throws Exception {

    if (!m_weightByConfidence) {
      TINY = 0.0;
    }

    if (instances.classIndex() == -1) {
      throw new Exception("No class attribute assigned");
    }
    if (!instances.classAttribute().isNominal()) {
      throw new UnsupportedClassTypeException("VFI: class attribute needs to be nominal!");
    }
    instances = new Instances(instances);
    instances.deleteWithMissingClass();

    m_ClassIndex = instances.classIndex();
    m_NumClasses = instances.numClasses();
    m_globalCounts = new double [m_NumClasses];
    m_maxEntrop = Math.log(m_NumClasses) / Math.log(2);

    m_Instances = new Instances(instances, 0); // Copy the structure for ref

    m_intervalBounds = 
      new double[instances.numAttributes()][2+(2*m_NumClasses)];

    for (int j = 0; j < instances.numAttributes(); j++) {
      boolean alt = false;
      for (int i = 0; i < m_NumClasses*2+2; i++) {
	if (i == 0) {
	  m_intervalBounds[j][i] = Double.NEGATIVE_INFINITY;
	} else if (i == m_NumClasses*2+1) {
	  m_intervalBounds[j][i] = Double.POSITIVE_INFINITY;
	} else {
	  if (alt) {
	    m_intervalBounds[j][i] = Double.NEGATIVE_INFINITY;
	    alt = false;
	  } else {
	    m_intervalBounds[j][i] = Double.POSITIVE_INFINITY;
	    alt = true;
	  }
	}
      }
    }

    // find upper and lower bounds for numeric attributes
    for (int j = 0; j < instances.numAttributes(); j++) {
      if (j != m_ClassIndex && instances.attribute(j).isNumeric()) {
	for (int i = 0; i < instances.numInstances(); i++) {
	  Instance inst = instances.instance(i);
	  if (!inst.isMissing(j)) {
	    if (inst.value(j) < 
		m_intervalBounds[j][((int)inst.classValue()*2+1)]) {
	      m_intervalBounds[j][((int)inst.classValue()*2+1)] = 
		inst.value(j);
	    }
	    if (inst.value(j) > 
		m_intervalBounds[j][((int)inst.classValue()*2+2)]) {
	      m_intervalBounds[j][((int)inst.classValue()*2+2)] = 
		inst.value(j);
	    }
	  }
	}
      }
    }

    m_counts = new double [instances.numAttributes()][][];

    // sort intervals
    for (int i = 0 ; i < instances.numAttributes(); i++) {
      if (instances.attribute(i).isNumeric()) {
	int [] sortedIntervals = Utils.sort(m_intervalBounds[i]);
	// remove any duplicate bounds
	int count = 1;
	for (int j = 1; j < sortedIntervals.length; j++) {
	  if (m_intervalBounds[i][sortedIntervals[j]] != 
	      m_intervalBounds[i][sortedIntervals[j-1]]) {
	    count++;
	  }
	}
	double [] reordered = new double [count];
	count = 1;
	reordered[0] = m_intervalBounds[i][sortedIntervals[0]];
	for (int j = 1; j < sortedIntervals.length; j++) {
	   if (m_intervalBounds[i][sortedIntervals[j]] != 
	      m_intervalBounds[i][sortedIntervals[j-1]]) {
	     reordered[count] =  m_intervalBounds[i][sortedIntervals[j]];
	     count++;
	   }
	}
	m_intervalBounds[i] = reordered;
	m_counts[i] = new double [count][m_NumClasses];
      } else if (i != m_ClassIndex) { // nominal attribute
	m_counts[i] = 
	  new double [instances.attribute(i).numValues()][m_NumClasses];
      }
    }

    // collect class counts
    for (int i = 0; i < instances.numInstances(); i++) {
      Instance inst = instances.instance(i);
      m_globalCounts[(int)instances.instance(i).classValue()] += inst.weight();
      for (int j = 0; j < instances.numAttributes(); j++) {
	if (!inst.isMissing(j) && j != m_ClassIndex) {
	  if (instances.attribute(j).isNumeric()) {
	    double val = inst.value(j);
	   
	    int k;
	    boolean ok = false;
	    for (k = m_intervalBounds[j].length-1; k >= 0; k--) {
	      if (val > m_intervalBounds[j][k]) {
		ok = true;
		m_counts[j][k][(int)inst.classValue()] += inst.weight();
		break;
	      } else if (val == m_intervalBounds[j][k]) {
		ok = true;
		m_counts[j][k][(int)inst.classValue()] += 
		  (inst.weight() / 2.0);
		m_counts[j][k-1][(int)inst.classValue()] += 
		  (inst.weight() / 2.0);;
		break;
	      }
	    }
	   
	  } else {
	    // nominal attribute
	    m_counts[j][(int)inst.value(j)][(int)inst.classValue()] += 
	      inst.weight();;
	  }
	}
      }
    }
  }

  /**
   * Returns a description of this classifier.
   *
   * @return a description of this classifier as a string.
   */
  public String toString() {
    if (m_Instances == null) {
      return "FVI: Classifier not built yet!";
    }
    StringBuffer sb = 
      new StringBuffer("Voting feature intervals classifier\n");

    /* Output the intervals and class counts for each attribute */
    /*    for (int i = 0; i < m_Instances.numAttributes(); i++) {
      if (i != m_ClassIndex) {
	sb.append("\n"+m_Instances.attribute(i).name()+" :\n");
	 if (m_Instances.attribute(i).isNumeric()) {
	   for (int j = 0; j < m_intervalBounds[i].length; j++) {
	     sb.append(m_intervalBounds[i][j]).append("\n");
	     if (j != m_intervalBounds[i].length-1) {
	       for (int k = 0; k < m_NumClasses; k++) {
		 sb.append(m_counts[i][j][k]+" ");
	       }
	     }
	     sb.append("\n");
	   }
	 } else {
	   for (int j = 0; j < m_Instances.attribute(i).numValues(); j++) {
	     sb.append(m_Instances.attribute(i).value(j)).append("\n");
	     for (int k = 0; k < m_NumClasses; k++) {
	       sb.append(m_counts[i][j][k]+" ");
	     }
	     sb.append("\n");
	   }
	 }
      }
      } */
    return sb.toString();
  }

  /**
   * Classifies the given test instance.
   *
   * @param instance the instance to be classified
   * @return the predicted class for the instance 
   * @exception Exception if the instance can't be classified
   */
  public double [] distributionForInstance(Instance instance) 
    throws Exception {
    double [] dist = new double[m_NumClasses];
    double [] temp = new double[m_NumClasses];
    double totalWeight = 0.0;
    double weight = 1.0;


    for (int i = 0; i < instance.numAttributes(); i++) {
      if (i != m_ClassIndex && !instance.isMissing(i)) {
	double val = instance.value(i);
	boolean ok = false;
	if (instance.attribute(i).isNumeric()) {
	  int k;
	  for (k = m_intervalBounds[i].length-1; k >= 0; k--) {
	    if (val > m_intervalBounds[i][k]) {
	      for (int j = 0; j < m_NumClasses; j++) {
		if (m_globalCounts[j] > 0) {
		  temp[j] = ((m_counts[i][k][j]+TINY) / 
			     (m_globalCounts[j]+TINY));
		}
	      }
	      ok = true;
	      break;
	    } else if (val == m_intervalBounds[i][k]) {
	      for (int j = 0; j < m_NumClasses; j++) {
		if (m_globalCounts[j] > 0) {
		  temp[j] = ((m_counts[i][k][j] + m_counts[i][k-1][j]) / 2.0) +
		    TINY;
		  temp[j] /= (m_globalCounts[j]+TINY);
		}
	      }
	      ok = true;
	      break;
	    }
	  }
	  if (!ok) {
	    throw new Exception("This shouldn't happen");
	  }
	} else { // nominal attribute
	  ok = true;
	  for (int j = 0; j < m_NumClasses; j++) {
	    if (m_globalCounts[j] > 0) {
	      temp[j] = ((m_counts[i][(int)val][j]+TINY) / 
			 (m_globalCounts[j]+TINY));
	    }
	  }	  
	}
	
	double sum = Utils.sum(temp);
	if (sum <= 0) {
	  for (int j = 0; j < temp.length; j++) {
	    temp[j] = 1.0 / (double)temp.length;
	  }
	} else {
	  Utils.normalize(temp, sum);
	}

	if (m_weightByConfidence) {
	  weight = weka.core.ContingencyTables.entropy(temp);
	  weight = Math.pow(weight, m_bias);
	  if (weight < 1.0) {
	    weight = 1.0;
	  }
	}

	for (int j = 0; j < m_NumClasses; j++) {
	  dist[j] += (temp[j] * weight);
	}
      }
    }
   
    double sum = Utils.sum(dist);
    if (sum <= 0) {
      for (int j = 0; j < dist.length; j++) {
	dist[j] = 1.0 / (double)dist.length;
      }
      return dist;
    } else {
      Utils.normalize(dist, sum);
      return dist;
    }
  }
	
  /**
   * Main method for testing this class.
   *
   * @param args should contain command line arguments for evaluation
   * (see Evaluation).
   */
  public static void main(String [] args) {
    try {
      System.out.println(Evaluation.evaluateModel(new VFI(), args));
    } catch (Exception e) {
      e.printStackTrace();
    }
  }
}