decisiontable.java

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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 2 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, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    DecisionTable.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.rules;

import weka.attributeSelection.ASSearch;
import weka.attributeSelection.BestFirst;
import weka.attributeSelection.SubsetEvaluator;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.lazy.IBk;
import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

import java.util.Arrays;
import java.util.BitSet;
import java.util.Enumeration;
import java.util.Hashtable;
import java.util.Random;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * Class for building and using a simple decision table majority classifier.<br/>
 * <br/>
 * For more information see: <br/>
 * <br/>
 * Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;inproceedings{Kohavi1995,
 *    author = {Ron Kohavi},
 *    booktitle = {8th European Conference on Machine Learning},
 *    pages = {174-189},
 *    publisher = {Springer},
 *    title = {The Power of Decision Tables},
 *    year = {1995}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -S &lt;search method specification&gt;
 *  Full class name of search method, followed
 *  by its options.
 *  eg: "weka.attributeSelection.BestFirst -D 1"
 *  (default weka.attributeSelection.BestFirst)</pre>
 * 
 * <pre> -X &lt;number of folds&gt;
 *  Use cross validation to evaluate features.
 *  Use number of folds = 1 for leave one out CV.
 *  (Default = leave one out CV)</pre>
 * 
 * <pre> -E &lt;acc | rmse | mae | auc&gt;
 *  Performance evaluation measure to use for selecting attributes.
 *  (Default = accuracy for discrete class and rmse for numeric class)</pre>
 * 
 * <pre> -I
 *  Use nearest neighbour instead of global table majority.</pre>
 * 
 * <pre> -R
 *  Display decision table rules.
 * </pre>
 * 
 * <pre> 
 * Options specific to search method weka.attributeSelection.BestFirst:
 * </pre>
 * 
 * <pre> -P &lt;start set&gt;
 *  Specify a starting set of attributes.
 *  Eg. 1,3,5-7.</pre>
 * 
 * <pre> -D &lt;0 = backward | 1 = forward | 2 = bi-directional&gt;
 *  Direction of search. (default = 1).</pre>
 * 
 * <pre> -N &lt;num&gt;
 *  Number of non-improving nodes to
 *  consider before terminating search.</pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Size of lookup cache for evaluated subsets.
 *  Expressed as a multiple of the number of
 *  attributes in the data set. (default = 1)</pre>
 * 
 <!-- options-end -->
 *
 * @author Mark Hall ([e-mail])
 * @version $Revision: 1.44 $ 
 */
public class DecisionTable 
  extends Classifier 
  implements OptionHandler, WeightedInstancesHandler, 
             AdditionalMeasureProducer, TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = 2888557078165701326L;

  /** The hashtable used to hold training instances */
  protected Hashtable m_entries;

  /** The class priors to use when there is no match in the table */
  protected double [] m_classPriorCounts;
  protected double [] m_classPriors;

  /** Holds the final feature set */
  protected int [] m_decisionFeatures;

  /** Discretization filter */
  protected Filter m_disTransform;

  /** Filter used to remove columns discarded by feature selection */
  protected Remove m_delTransform;

  /** IB1 used to classify non matching instances rather than majority class */
  protected IBk m_ibk;

  /** Holds the original training instances */
  protected Instances m_theInstances;

  /** Holds the final feature selected set of instances */
  protected Instances m_dtInstances;

  /** The number of attributes in the dataset */
  protected int m_numAttributes;

  /** The number of instances in the dataset */
  private int m_numInstances;

  /** Class is nominal */
  protected boolean m_classIsNominal;

  /** Use the IBk classifier rather than majority class */
  protected boolean m_useIBk;

  /** Display Rules */
  protected boolean m_displayRules;

  /** Number of folds for cross validating feature sets */
  private int m_CVFolds;

  /** Random numbers for use in cross validation */
  private Random m_rr;

  /** Holds the majority class */
  protected double m_majority;

  /** The search method to use */
  protected ASSearch m_search = new BestFirst();

  /** Our own internal evaluator */
  protected SubsetEvaluator m_evaluator;

  /** The evaluation object used to evaluate subsets */
  protected Evaluation m_evaluation;

  /** default is accuracy for discrete class and RMSE for numeric class */
  public static final int EVAL_DEFAULT = 1;
  public static final int EVAL_ACCURACY = 2;
  public static final int EVAL_RMSE = 3;
  public static final int EVAL_MAE = 4;
  public static final int EVAL_AUC = 5;

  public static final Tag [] TAGS_EVALUATION = {
    new Tag(EVAL_DEFAULT, "Default: accuracy (discrete class); RMSE (numeric class)"),
    new Tag(EVAL_ACCURACY, "Accuracy (discrete class only"),
    new Tag(EVAL_RMSE, "RMSE (of the class probabilities for discrete class)"),
    new Tag(EVAL_MAE, "MAE (of the class probabilities for discrete class)"),
    new Tag(EVAL_AUC, "AUC (area under the ROC curve - discrete class only)")
  };

  protected int m_evaluationMeasure = EVAL_DEFAULT;

  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {

    return  
    "Class for building and using a simple decision table majority "
    + "classifier.\n\n"
    + "For more information see: \n\n"
    + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation 	result;

    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR, "Ron Kohavi");
    result.setValue(Field.TITLE, "The Power of Decision Tables");
    result.setValue(Field.BOOKTITLE, "8th European Conference on Machine Learning");
    result.setValue(Field.YEAR, "1995");
    result.setValue(Field.PAGES, "174-189");
    result.setValue(Field.PUBLISHER, "Springer");

    return result;
  }
  
  /**
   * Inserts an instance into the hash table
   *
   * @param inst instance to be inserted
   * @param instA to create the hash key from
   * @throws Exception if the instance can't be inserted
   */
  private void insertIntoTable(Instance inst, double [] instA)
  throws Exception {

    double [] tempClassDist2;
    double [] newDist;
    DecisionTableHashKey thekey;

    if (instA != null) {
      thekey = new DecisionTableHashKey(instA);
    } else {
      thekey = new DecisionTableHashKey(inst, inst.numAttributes(), false);
    }

    // see if this one is already in the table
    tempClassDist2 = (double []) m_entries.get(thekey);
    if (tempClassDist2 == null) {
      if (m_classIsNominal) {
	newDist = new double [m_theInstances.classAttribute().numValues()];
	
	//Leplace estimation
	for (int i = 0; i < m_theInstances.classAttribute().numValues(); i++) {
	  newDist[i] = 1.0;
	}
	
	newDist[(int)inst.classValue()] = inst.weight();

	// add to the table
	m_entries.put(thekey, newDist);
      } else {
	newDist = new double [2];
	newDist[0] = inst.classValue() * inst.weight();
	newDist[1] = inst.weight();

	// add to the table
	m_entries.put(thekey, newDist);
      }
    } else { 

      // update the distribution for this instance
      if (m_classIsNominal) {
	tempClassDist2[(int)inst.classValue()]+=inst.weight();

	// update the table
	m_entries.put(thekey, tempClassDist2);
      } else  {
	tempClassDist2[0] += (inst.classValue() * inst.weight());
	tempClassDist2[1] += inst.weight();

	// update the table
	m_entries.put(thekey, tempClassDist2);
      }
    }
  }

  /**
   * Classifies an instance for internal leave one out cross validation
   * of feature sets
   *
   * @param instance instance to be "left out" and classified
   * @param instA feature values of the selected features for the instance
   * @return the classification of the instance
   * @throws Exception if something goes wrong
   */
  double evaluateInstanceLeaveOneOut(Instance instance, double [] instA)
  throws Exception {

    DecisionTableHashKey thekey;
    double [] tempDist;
    double [] normDist;

    thekey = new DecisionTableHashKey(instA);
    if (m_classIsNominal) {

      // if this one is not in the table
      if ((tempDist = (double [])m_entries.get(thekey)) == null) {
	throw new Error("This should never happen!");
      } else {
	normDist = new double [tempDist.length];
	System.arraycopy(tempDist,0,normDist,0,tempDist.length);
	normDist[(int)instance.classValue()] -= instance.weight();

	// update the table
	// first check to see if the class counts are all zero now
	boolean ok = false;
	for (int i=0;i<normDist.length;i++) {
	  if (Utils.gr(normDist[i],1.0)) {
	    ok = true;
	    break;
	  }
	}

//	downdate the class prior counts
	m_classPriorCounts[(int)instance.classValue()] -= 
	  instance.weight();
	double [] classPriors = m_classPriorCounts.clone();
	Utils.normalize(classPriors);
	if (!ok) { // majority class
	  normDist = classPriors;
	}

	m_classPriorCounts[(int)instance.classValue()] += 
	  instance.weight();

	//if (ok) {
	Utils.normalize(normDist);
	if (m_evaluationMeasure == EVAL_AUC) {
	  m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, instance);						
	} else {
	  m_evaluation.evaluateModelOnce(normDist, instance);
	}
	return Utils.maxIndex(normDist);
	/*} else {
	  normDist = new double [normDist.length];
	  normDist[(int)m_majority] = 1.0;
	  if (m_evaluationMeasure == EVAL_AUC) {
	    m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, instance);						
	  } else {
	    m_evaluation.evaluateModelOnce(normDist, instance);
	  }
	  return m_majority;
	} */
      }
      //      return Utils.maxIndex(tempDist);
    } else {

      // see if this one is already in the table
      if ((tempDist = (double[])m_entries.get(thekey)) != null) {
	normDist = new double [tempDist.length];
	System.arraycopy(tempDist,0,normDist,0,tempDist.length);
	normDist[0] -= (instance.classValue() * instance.weight());
	normDist[1] -= instance.weight();
	if (Utils.eq(normDist[1],0.0)) {
	  double [] temp = new double[1];
	  temp[0] = m_majority;
	  m_evaluation.evaluateModelOnce(temp, instance);
	  return m_majority;
	} else {
	  double [] temp = new double[1];
	  temp[0] = normDist[0] / normDist[1];
	  m_evaluation.evaluateModelOnce(temp, instance);
	  return temp[0];
	}
      } else {
	throw new Error("This should never happen!");
      }
    }

    // shouldn't get here 
    // return 0.0;
  }

  /**
   * Calculates the accuracy on a test fold for internal cross validation
   * of feature sets
   *
   * @param fold set of instances to be "left out" and classified
   * @param fs currently selected feature set
   * @return the accuracy for the fold
   * @throws Exception if something goes wrong
   */
  double evaluateFoldCV(Instances fold, int [] fs) throws Exception {

    int i;
    int ruleCount = 0;
    int numFold = fold.numInstances();
    int numCl = m_theInstances.classAttribute().numValues();
    double [][] class_distribs = new double [numFold][numCl];
    double [] instA = new double [fs.length];
    double [] normDist;
    DecisionTableHashKey thekey;
    double acc = 0.0;
    int classI = m_theInstances.classIndex();
    Instance inst;

    if (m_classIsNominal) {
      normDist = new double [numCl];
    } else {
      normDist = new double [2];
    }

    // first *remove* instances
    for (i=0;i<numFold;i++) {
      inst = fold.instance(i);
      for (int j=0;j<fs.length;j++) {
	if (fs[j] == classI) {
	  instA[j] = Double.MAX_VALUE; // missing for the class
	} else if (inst.isMissing(fs[j])) {
	  instA[j] = Double.MAX_VALUE;
	} else{
	  instA[j] = inst.value(fs[j]);
	}
      }
      thekey = new DecisionTableHashKey(instA);
      if ((class_distribs[i] = (double [])m_entries.get(thekey)) == null) {
	throw new Error("This should never happen!");
      } else {
	if (m_classIsNominal) {
	  class_distribs[i][(int)inst.classValue()] -= inst.weight();
	} else {
	  class_distribs[i][0] -= (inst.classValue() * inst.weight());
	  class_distribs[i][1] -= inst.weight();
	}
	ruleCount++;
      }
      m_classPriorCounts[(int)inst.classValue()] -= 
	inst.weight();	
    }
    double [] classPriors = m_classPriorCounts.clone();
    Utils.normalize(classPriors);

    // now classify instances