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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 *    NBTreeModelSelection.java
 *    Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.j48;

import java.util.Enumeration;

import weka.core.Attribute;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 * Class for selecting a NB tree split.
 * 
 * @author Mark Hall ([email protected])
 * @version $Revision: 10531 $
 */
public class NBTreeModelSelection extends ModelSelection {

  /** for serialization */
  private static final long serialVersionUID = 990097748931976704L;

  /** Minimum number of objects in interval. */
  protected final int m_minNoObj;

  /** All the training data */
  protected Instances m_allData; //

  /**
   * Initializes the split selection method with the given parameters.
   * 
   * @param minNoObj minimum number of instances that have to occur in at least
   *          two subsets induced by split
   * @param allData FULL training dataset (necessary for selection of split
   *          points).
   */
  public NBTreeModelSelection(int minNoObj, Instances allData) {
    m_minNoObj = minNoObj;
    m_allData = allData;
  }

  /**
   * Sets reference to training data to null.
   */
  public void cleanup() {

    m_allData = null;
  }

  /**
   * Selects NBTree-type split for the given dataset.
   */
  @Override
  public final ClassifierSplitModel selectModel(Instances data) {

    double globalErrors = 0;

    double minResult;
    NBTreeSplit[] currentModel;
    NBTreeSplit bestModel = null;
    NBTreeNoSplit noSplitModel = null;
    int validModels = 0;
    Distribution checkDistribution;
    Attribute attribute;
    double sumOfWeights;
    int i;

    try {
      // build the global model at this node
      noSplitModel = new NBTreeNoSplit();
      noSplitModel.buildClassifier(data);
      if (data.numInstances() < 5) {
        return noSplitModel;
      }

      // evaluate it
      globalErrors = noSplitModel.getErrors();
      if (globalErrors == 0) {
        return noSplitModel;
      }

      // Check if all Instances belong to one class or if not
      // enough Instances to split.
      checkDistribution = new Distribution(data);
      if (Utils.sm(checkDistribution.total(), m_minNoObj)
        || Utils.eq(checkDistribution.total(),
          checkDistribution.perClass(checkDistribution.maxClass()))) {
        return noSplitModel;
      }

      // Check if all attributes are nominal and have a
      // lot of values.
      if (m_allData != null) {
        Enumeration enu = data.enumerateAttributes();
        while (enu.hasMoreElements()) {
          attribute = enu.nextElement();
          if ((attribute.isNumeric())
            || (Utils.sm(attribute.numValues(),
              (0.3 * m_allData.numInstances())))) {
            break;
          }
        }
      }

      currentModel = new NBTreeSplit[data.numAttributes()];
      sumOfWeights = data.sumOfWeights();

      // For each attribute.
      for (i = 0; i < data.numAttributes(); i++) {

        // Apart from class attribute.
        if (i != (data).classIndex()) {

          // Get models for current attribute.
          currentModel[i] = new NBTreeSplit(i, m_minNoObj, sumOfWeights);
          currentModel[i].setGlobalModel(noSplitModel);
          currentModel[i].buildClassifier(data);

          // Check if useful split for current attribute
          // exists and check for enumerated attributes with
          // a lot of values.
          if (currentModel[i].checkModel()) {
            validModels++;
          }
        } else {
          currentModel[i] = null;
        }
      }

      // Check if any useful split was found.
      if (validModels == 0) {
        return noSplitModel;
      }

      // Find "best" attribute to split on.
      minResult = globalErrors;
      for (i = 0; i < data.numAttributes(); i++) {
        if ((i != (data).classIndex()) && (currentModel[i].checkModel())) {
          /*
           * System.err.println("Errors for "+data.attribute(i).name()+" "+
           * currentModel[i].getErrors());
           */
          if (currentModel[i].getErrors() < minResult) {
            bestModel = currentModel[i];
            minResult = currentModel[i].getErrors();
          }
        }
      }
      // System.exit(1);
      // Check if useful split was found.

      if (((globalErrors - minResult) / globalErrors) < 0.05) {
        return noSplitModel;
      }

      /*
       * if (bestModel == null) {
       * System.err.println("This shouldn't happen! glob : "+globalErrors+
       * " minRes : "+minResult); System.exit(1); }
       */
      // Set the global model for the best split
      // bestModel.setGlobalModel(noSplitModel);

      return bestModel;
    } catch (Exception e) {
      e.printStackTrace();
    }
    return null;
  }

  /**
   * Selects NBTree-type split for the given dataset.
   */
  @Override
  public final ClassifierSplitModel selectModel(Instances train, Instances test) {

    return selectModel(train);
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10531 $");
  }
}