gistrainer.java

最大熵分类器

/////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2001 Jason Baldridge, Gann Bierner, and Tom Morton
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library 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 Lesser General Public
// License along with this program; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
//////////////////////////////////////////////////////////////////////////////   
package opennlp.maxent;


/**
 * An implementation of Generalized Iterative Scaling.  The reference paper
 * for this implementation was Adwait Ratnaparkhi's tech report at the
 * University of Pennsylvania's Institute for Research in Cognitive Science,
 * and is available at <a href ="ftp://ftp.cis.upenn.edu/pub/ircs/tr/97-08.ps.Z"><code>ftp://ftp.cis.upenn.edu/pub/ircs/tr/97-08.ps.Z</code></a>. 
 *
 * The slack parameter used in the above implementation has been removed by default
 * from the computation and a method for updating with Gaussian smoothing has been
 * added per Investigating GIS and Smoothing for Maximum Entropy Taggers, Clark and Curran (2002).  
 * <a href="http://acl.ldc.upenn.edu/E/E03/E03-1071.pdf"><code>http://acl.ldc.upenn.edu/E/E03/E03-1071.pdf</code></a>
 * The slack parameter can be used by setting <code>useSlackParameter</code> to true.
 * Gaussian smoothing can be used by setting <code>useGaussianSmoothing</code> to true. 
 * 
 * A prior can be used to train models which converge to the distribution which minimizes the
 * relative entropy between the distribution specified by the emperical constraints of the training
 * data and the specified prior.  By default, the uniform distribution is used as the prior.
 *    
 * @author Tom Morton
 * @author  Jason Baldridge
 * @version $Revision: 1.29 $, $Date: 2008/09/21 03:20:15 $
 */
class GISTrainer {

  /**
   * Specifies whether unseen context/outcome pairs should be estimated as occur very infrequently.
   */
  private boolean useSimpleSmoothing = false;
  /**
   * Specifies whether a slack parameter should be used in the model.
   */
  private boolean useSlackParameter = false;
  /** Specified whether parameter updates should prefer a distribution of parameters which
   * is gaussian.
   */
  private boolean useGaussianSmoothing = false;
  private double sigma = 2.0;

  // If we are using smoothing, this is used as the "number" of
  // times we want the trainer to imagine that it saw a feature that it
  // actually didn't see.  Defaulted to 0.1.
  private double _smoothingObservation = 0.1;

  private boolean printMessages = false;

  /** Number of unique events which occured in the event set. */
  private int numUniqueEvents; 
  /** Number of predicates. */
  private int numPreds; 
  /** Number of outcomes. */
  private int numOutcomes; 

  /** Records the array of predicates seen in each event. */
  private int[][] contexts;
  
  /** The value associated with each context. If null then context values are assumes to be 1. */
  private float[][] values;
  
  /** List of outcomes for each event i, in context[i]. */
  private int[] outcomeList;

  /** Records the num of times an event has been seen for each event i, in context[i]. */
  private int[] numTimesEventsSeen;
  
  /** The number of times a predicate occured in the training data. */
  private int[] predicateCounts;
  
  private int cutoff;

  /** Stores the String names of the outcomes.  The GIS only tracks outcomes
   as ints, and so this array is needed to save the model to disk and
   thereby allow users to know what the outcome was in human
   understandable terms. */
  private String[] outcomeLabels;

  /** Stores the String names of the predicates. The GIS only tracks
   predicates as ints, and so this array is needed to save the model to
   disk and thereby allow users to know what the outcome was in human
   understandable terms. */
  private String[] predLabels;

  /** Stores the observed expected values of the features based on training data. */
  private MutableContext[] observedExpects;

  /** Stores the estimated parameter value of each predicate during iteration */
  private MutableContext[] params;

  /** Stores the expected values of the features based on the current models */
  private MutableContext[] modelExpects;
  
  /** This is the prior distribution that the model uses for training. */
  private Prior prior;


  /** Observed expectation of correction feature. */
  private double cfObservedExpect;
  /** A global variable for the models expected value of the correction feature. */
  private double CFMOD;

  private final double NEAR_ZERO = 0.01;
  private final double LLThreshold = 0.0001;

  /** Stores the output of the current model on a single event durring
   *  training.  This we be reset for every event for every itteration.  */
  double[] modelDistribution;
  /** Stores the number of features that get fired per event. */
  int[] numfeats;
  /** Initial probability for all outcomes. */
  
  EvalParameters evalParams;
  /**
   * Creates a new <code>GISTrainer</code> instance which does
   * not print progress messages about training to STDOUT.
   *
   */
  GISTrainer() {
    super();
  }

  /**
   * Creates a new <code>GISTrainer</code> instance.
   *
   * @param printMessages sends progress messages about training to
   *                      STDOUT when true; trains silently otherwise.
   */
  GISTrainer(boolean printMessages) {
    this();
    this.printMessages = printMessages;
  }

  /**
   * Sets whether this trainer will use smoothing while training the model.
   * This can improve model accuracy, though training will potentially take
   * longer and use more memory.  Model size will also be larger.
   *
   * @param smooth true if smoothing is desired, false if not
   */
  public void setSmoothing(boolean smooth) {
    useSimpleSmoothing = smooth;
  }

  /**
   * Sets whether this trainer will use smoothing while training the model.
   * This can improve model accuracy, though training will potentially take
   * longer and use more memory.  Model size will also be larger.
   *
   * @param timesSeen the "number" of times we want the trainer to imagine
   *                  it saw a feature that it actually didn't see
   */
  public void setSmoothingObservation(double timesSeen) {
    _smoothingObservation = timesSeen;
  }
  

  /**
   * Trains a GIS model on the event in the specified event stream, using the specified number
   * of iterations and the specified count cutoff.
   * @param eventStream A stream of all events.
   * @param iterations The number of iterations to use for GIS.
   * @param cutoff The number of times a feature must occur to be included.
   * @return A GIS model trained with specified 
   */
  public GISModel trainModel(EventStream eventStream, int iterations, int cutoff) {
    return trainModel(iterations, new OnePassDataIndexer(eventStream,cutoff),cutoff);
  }
  
  /**
   * Train a model using the GIS algorithm.
   *
   * @param iterations  The number of GIS iterations to perform.
   * @param di The data indexer used to compress events in memory.
   * @return The newly trained model, which can be used immediately or saved
   *         to disk using an opennlp.maxent.io.GISModelWriter object.
   */
  public GISModel trainModel(int iterations, DataIndexer di, int cutoff) {
    return trainModel(iterations,di,new UniformPrior(),cutoff);
  }

  /**
   * Train a model using the GIS algorithm.
   *
   * @param iterations  The number of GIS iterations to perform.
   * @param di The data indexer used to compress events in memory.
   * @param modelPrior The prior distribution used to train this model.
   * @return The newly trained model, which can be used immediately or saved
   *         to disk using an opennlp.maxent.io.GISModelWriter object.
   */
  public GISModel trainModel(int iterations, DataIndexer di, Prior modelPrior, int cutoff) {
    /************** Incorporate all of the needed info ******************/
    display("Incorporating indexed data for training...  \n");
    contexts = di.getContexts();
    values = di.getValues();
    this.cutoff = cutoff;
    predicateCounts = di.getPredCounts();
    numTimesEventsSeen = di.getNumTimesEventsSeen();
    numUniqueEvents = contexts.length;
    this.prior = modelPrior;
    //printTable(contexts);

    // determine the correction constant and its inverse
    int correctionConstant = 1;
    for (int ci = 0; ci < contexts.length; ci++) {
      if (values == null || values[ci] == null) {
        if (contexts[ci].length > correctionConstant) {
          correctionConstant = contexts[ci].length;
        }
      }
      else {
        float cl = values[ci][0];
        for (int vi=1;vi<values[ci].length;vi++) {
          cl+=values[ci][vi];
        }
        
        if (cl > correctionConstant) {
          correctionConstant=(int) Math.ceil(cl);
        }
      }
    }
    display("done.\n");

    outcomeLabels = di.getOutcomeLabels();
    outcomeList = di.getOutcomeList();
    numOutcomes = outcomeLabels.length;

    predLabels = di.getPredLabels();
    prior.setLabels(outcomeLabels,predLabels);
    numPreds = predLabels.length;

    display("\tNumber of Event Tokens: " + numUniqueEvents + "\n");
    display("\t    Number of Outcomes: " + numOutcomes + "\n");
    display("\t  Number of Predicates: " + numPreds + "\n");

    // set up feature arrays
    float[][] predCount = new float[numPreds][numOutcomes];
    for (int ti = 0; ti < numUniqueEvents; ti++) {
      for (int j = 0; j < contexts[ti].length; j++) {
        if (values != null && values[ti] != null) {