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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 .
 */

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

package weka.classifiers.trees.j48;

import java.util.Random;

import weka.classifiers.bayes.NaiveBayesUpdateable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;

/**
 * Class implementing a NBTree split on an attribute.
 * 
 * @author Mark Hall ([email protected])
 * @version $Revision: 10531 $
 */
public class NBTreeSplit extends ClassifierSplitModel {

  /** for serialization */
  private static final long serialVersionUID = 8922627123884975070L;

  /** Desired number of branches. */
  protected int m_complexityIndex;

  /** Attribute to split on. */
  protected final int m_attIndex;

  /** The sum of the weights of the instances. */
  protected final double m_sumOfWeights;

  /**
   * The weight of the instances incorrectly classified by the naive bayes
   * models arising from this split
   */
  protected double m_errors;

  protected C45Split m_c45S;

  /** The global naive bayes model for this node */
  NBTreeNoSplit m_globalNB;

  /**
   * Initializes the split model.
   */
  public NBTreeSplit(int attIndex, int minNoObj, double sumOfWeights) {

    // Get index of attribute to split on.
    m_attIndex = attIndex;

    // Set the sum of the weights
    m_sumOfWeights = sumOfWeights;

  }

  /**
   * Creates a NBTree-type split on the given data. Assumes that none of the
   * class values is missing.
   * 
   * @exception Exception if something goes wrong
   */
  @Override
  public void buildClassifier(Instances trainInstances) throws Exception {

    // Initialize the remaining instance variables.
    m_numSubsets = 0;
    m_errors = 0;
    if (m_globalNB != null) {
      m_errors = m_globalNB.getErrors();
    }

    // Different treatment for enumerated and numeric
    // attributes.
    if (trainInstances.attribute(m_attIndex).isNominal()) {
      m_complexityIndex = trainInstances.attribute(m_attIndex).numValues();
      handleEnumeratedAttribute(trainInstances);
    } else {
      m_complexityIndex = 2;
      trainInstances.sort(trainInstances.attribute(m_attIndex));
      handleNumericAttribute(trainInstances);
    }
  }

  /**
   * Returns index of attribute for which split was generated.
   */
  public final int attIndex() {

    return m_attIndex;
  }

  /**
   * Creates split on enumerated attribute.
   * 
   * @exception Exception if something goes wrong
   */
  private void handleEnumeratedAttribute(Instances trainInstances)
    throws Exception {

    m_c45S = new C45Split(m_attIndex, 2, m_sumOfWeights, true);
    m_c45S.buildClassifier(trainInstances);
    if (m_c45S.numSubsets() == 0) {
      return;
    }
    m_errors = 0;
    Instance instance;

    Instances[] trainingSets = new Instances[m_complexityIndex];
    for (int i = 0; i < m_complexityIndex; i++) {
      trainingSets[i] = new Instances(trainInstances, 0);
    }
    /*
     * m_distribution = new Distribution(m_complexityIndex,
     * trainInstances.numClasses());
     */
    int subset;
    for (int i = 0; i < trainInstances.numInstances(); i++) {
      instance = trainInstances.instance(i);
      subset = m_c45S.whichSubset(instance);
      if (subset > -1) {
        trainingSets[subset].add((Instance) instance.copy());
      } else {
        double[] weights = m_c45S.weights(instance);
        for (int j = 0; j < m_complexityIndex; j++) {
          try {
            Instance temp = (Instance) instance.copy();
            if (weights.length == m_complexityIndex) {
              temp.setWeight(temp.weight() * weights[j]);
            } else {
              temp.setWeight(temp.weight() / m_complexityIndex);
            }
            trainingSets[j].add(temp);
          } catch (Exception ex) {
            ex.printStackTrace();
            System.err.println("*** " + m_complexityIndex);
            System.err.println(weights.length);
            System.exit(1);
          }
        }
      }
    }

    /*
     * // compute weights (weights of instances per subset m_weights = new
     * double [m_complexityIndex]; for (int i = 0; i < m_complexityIndex; i++) {
     * m_weights[i] = trainingSets[i].sumOfWeights(); }
     * Utils.normalize(m_weights);
     */

    /*
     * // Only Instances with known values are relevant. Enumeration enu =
     * trainInstances.enumerateInstances(); while (enu.hasMoreElements()) {
     * instance = (Instance) enu.nextElement(); if
     * (!instance.isMissing(m_attIndex)) { //
     * m_distribution.add((int)instance.value(m_attIndex),instance);
     * trainingSets[(int)instances.value(m_attIndex)].add(instance); } else { //
     * add these to the error count m_errors += instance.weight(); } }
     */

    Random r = new Random(1);
    int minNumCount = 0;
    for (int i = 0; i < m_complexityIndex; i++) {
      if (trainingSets[i].numInstances() >= 5) {
        minNumCount++;
        // Discretize the sets
        Discretize disc = new Discretize();
        disc.setInputFormat(trainingSets[i]);
        trainingSets[i] = Filter.useFilter(trainingSets[i], disc);

        trainingSets[i].randomize(r);
        trainingSets[i].stratify(5);
        NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
        fullModel.buildClassifier(trainingSets[i]);

        // add the errors for this branch of the split
        m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
        // if fewer than min obj then just count them as errors
        for (int j = 0; j < trainingSets[i].numInstances(); j++) {
          m_errors += trainingSets[i].instance(j).weight();
        }
      }
    }

    // Check if there are at least five instances in at least two of the subsets
    // subsets.
    if (minNumCount > 1) {
      m_numSubsets = m_complexityIndex;
    }
  }

  /**
   * Creates split on numeric attribute.
   * 
   * @exception Exception if something goes wrong
   */
  private void handleNumericAttribute(Instances trainInstances)
    throws Exception {

    m_c45S = new C45Split(m_attIndex, 2, m_sumOfWeights, true);
    m_c45S.buildClassifier(trainInstances);
    if (m_c45S.numSubsets() == 0) {
      return;
    }
    m_errors = 0;

    Instances[] trainingSets = new Instances[m_complexityIndex];
    trainingSets[0] = new Instances(trainInstances, 0);
    trainingSets[1] = new Instances(trainInstances, 0);
    int subset = -1;

    // populate the subsets
    for (int i = 0; i < trainInstances.numInstances(); i++) {
      Instance instance = trainInstances.instance(i);
      subset = m_c45S.whichSubset(instance);
      if (subset != -1) {
        trainingSets[subset].add((Instance) instance.copy());
      } else {
        double[] weights = m_c45S.weights(instance);
        for (int j = 0; j < m_complexityIndex; j++) {
          Instance temp = (Instance) instance.copy();
          if (weights.length == m_complexityIndex) {
            temp.setWeight(temp.weight() * weights[j]);
          } else {
            temp.setWeight(temp.weight() / m_complexityIndex);
          }
          trainingSets[j].add(temp);
        }
      }
    }

    /*
     * // compute weights (weights of instances per subset m_weights = new
     * double [m_complexityIndex]; for (int i = 0; i < m_complexityIndex; i++) {
     * m_weights[i] = trainingSets[i].sumOfWeights(); }
     * Utils.normalize(m_weights);
     */

    Random r = new Random(1);
    int minNumCount = 0;
    for (int i = 0; i < m_complexityIndex; i++) {
      if (trainingSets[i].numInstances() > 5) {
        minNumCount++;
        // Discretize the sets
        Discretize disc = new Discretize();
        disc.setInputFormat(trainingSets[i]);
        trainingSets[i] = Filter.useFilter(trainingSets[i], disc);

        trainingSets[i].randomize(r);
        trainingSets[i].stratify(5);
        NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
        fullModel.buildClassifier(trainingSets[i]);

        // add the errors for this branch of the split
        m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
        for (int j = 0; j < trainingSets[i].numInstances(); j++) {
          m_errors += trainingSets[i].instance(j).weight();
        }
      }
    }

    // Check if minimum number of Instances in at least two
    // subsets.
    if (minNumCount > 1) {
      m_numSubsets = m_complexityIndex;
    }
  }

  /**
   * Returns index of subset instance is assigned to. Returns -1 if instance is
   * assigned to more than one subset.
   * 
   * @exception Exception if something goes wrong
   */
  @Override
  public final int whichSubset(Instance instance) throws Exception {

    return m_c45S.whichSubset(instance);
  }

  /**
   * Returns weights if instance is assigned to more than one subset. Returns
   * null if instance is only assigned to one subset.
   */
  @Override
  public final double[] weights(Instance instance) {
    return m_c45S.weights(instance);
    // return m_weights;
  }

  /**
   * Returns a string containing java source code equivalent to the test made at
   * this node. The instance being tested is called "i".
   * 
   * @param index index of the nominal value tested
   * @param data the data containing instance structure info
   * @return a value of type 'String'
   */
  @Override
  public final String sourceExpression(int index, Instances data) {
    return m_c45S.sourceExpression(index, data);
  }

  /**
   * Prints the condition satisfied by instances in a subset.
   * 
   * @param index of subset
   * @param data training set.
   */
  @Override
  public final String rightSide(int index, Instances data) {
    return m_c45S.rightSide(index, data);
  }

  /**
   * Prints left side of condition..
   * 
   * @param data training set.
   */
  @Override
  public final String leftSide(Instances data) {

    return m_c45S.leftSide(data);
  }

  /**
   * Return the probability for a class value
   * 
   * @param classIndex the index of the class value
   * @param instance the instance to generate a probability for
   * @param theSubset the subset to consider
   * @return a probability
   * @exception Exception if an error occurs
   */
  @Override
  public double classProb(int classIndex, Instance instance, int theSubset)
    throws Exception {

    // use the global naive bayes model
    if (theSubset > -1) {
      return m_globalNB.classProb(classIndex, instance, theSubset);
    } else {
      throw new Exception("This shouldn't happen!!!");
    }
  }

  /**
   * Return the global naive bayes model for this node
   * 
   * @return a NBTreeNoSplit value
   */
  public NBTreeNoSplit getGlobalModel() {
    return m_globalNB;
  }

  /**
   * Set the global naive bayes model for this node
   * 
   * @param global a NBTreeNoSplit value
   */
  public void setGlobalModel(NBTreeNoSplit global) {
    m_globalNB = global;
  }

  /**
   * Return the errors made by the naive bayes models arising from this split.
   * 
   * @return a double value
   */
  public double getErrors() {
    return m_errors;
  }

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