dedupingprcurvecvresultproducersplit.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

/*
 *    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.
 */

/*
 *    DedupingPRCurveCVResultProducer.java
 *    Copyright (c) 2003 Mikhail Bilenko
 *
 */


package weka.experiment;

import java.util.*;
import java.io.*;
import weka.core.*;
import weka.core.OptionHandler;
import weka.core.Option;
import weka.core.Utils;
import weka.core.AdditionalMeasureProducer;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;


/**
 * N-fold cross-validation learning curve for
 * deduping applications
 *
 * @author Mikhail Bilenko */

public class  DedupingPRCurveCVResultProducerSplit
  implements ResultProducer, OptionHandler, AdditionalMeasureProducer {
  
  /** The dataset of interest */
  protected Instances m_instances;

  /** SVM-light can work in classification, regression and preference ranking modes */
  public static final int FOLD_CREATION_MODE_STRATIFIED = 1;
  public static final int FOLD_CREATION_MODE_RANDOM = 2;
  public static final Tag[] TAGS_FOLD_CREATION_MODE = {
    new Tag(FOLD_CREATION_MODE_STRATIFIED, "Stratified"),
    new Tag(FOLD_CREATION_MODE_RANDOM, "Random")
  };
  protected int m_foldCreationMode = FOLD_CREATION_MODE_STRATIFIED;

  /** The ResultListener to send results to */
  protected ResultListener m_resultListener = new CSVResultListener();

  /** The number of folds in the cross-validation */
  protected int m_numFolds = 2;

  /** Save raw output of split evaluators --- for debugging purposes */
  protected boolean m_debugOutput = false;

  /** The output zipper to use for saving raw splitEvaluator output */
  protected OutputZipper m_zipDest = null;

  /** The destination output file/directory for raw output */
  protected File m_outputFile = new File(
				new File(System.getProperty("user.dir")), 
				"splitEvalutorOut.zip");

  /** The separate training file if desired */
  protected String m_separateTrainingFile = new String("");

  /** The SplitEvaluator used to generate results */
  protected SplitEvaluator m_splitEvaluator = new DeduperSplitEvaluator();

  /** The names of any additional measures to look for in SplitEvaluators */
  protected String [] m_additionalMeasures = null;

  /** The specific points to plot, either integers representing specific numbers of training examples,
   * or decimal fractions representing percentages of the full training set*/
  protected double[] m_plotPoints = {0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,
				     0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0};

  /* The name of the key field containing the dataset name */
  public static String DATASET_FIELD_NAME = "Dataset";

  /* The name of the key field containing the run number */
  public static String RUN_FIELD_NAME = "Run";

  /* The name of the key field containing the fold number */
  public static String FOLD_FIELD_NAME = "Fold";

  /* The name of the result field containing the timestamp */
  public static String TIMESTAMP_FIELD_NAME = "Date_time";

  /* The name of the key field containing the learning rate step number */
  public static String RECALL_FIELD_NAME = "Fraction_instances";

  /**
   * Returns a string describing this result producer
   * @return a description of the result producer suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "Performs a learning-curve cross validation run using a supplied " +
      "deduping  split evaluator. Trains on increasing subsets of the training data for each split";
  }

  /**
   * Sets the dataset that results will be obtained for.
   *
   * @param instances a value of type 'Instances'.
   */
  public void setInstances(Instances instances) {
    m_instances = instances;
  }

  /**
   * Sets the object to send results of each run to.
   *
   * @param listener a value of type 'ResultListener'
   */
  public void setResultListener(ResultListener listener) {
    m_resultListener = listener;
  }

  /**
   * Set a list of method names for additional measures to look for
   * in SplitEvaluators. This could contain many measures (of which only a
   * subset may be produceable by the current SplitEvaluator) if an experiment
   * is the type that iterates over a set of properties.
   * @param additionalMeasures an array of measure names, null if none
   */
  public void setAdditionalMeasures(String [] additionalMeasures) {
    m_additionalMeasures = additionalMeasures;
    if (m_splitEvaluator != null) {
      System.err.println(" DedupingPRCurveCVResultProducerSplit: setting additional "
			 +"measures for "
			 +"split evaluator");
      m_splitEvaluator.setAdditionalMeasures(m_additionalMeasures);
    }
  }

  /**
   * Returns an enumeration of any additional measure names that might be
   * in the SplitEvaluator
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector();
    if (m_splitEvaluator instanceof AdditionalMeasureProducer) {
      Enumeration en = ((AdditionalMeasureProducer)m_splitEvaluator).
	enumerateMeasures();
      while (en.hasMoreElements()) {
	String mname = (String)en.nextElement();
	newVector.addElement(mname);
      }
    }
    return newVector.elements();
  }
  
  /**
   * Returns the value of the named measure
   * @param measureName the name of the measure to query for its value
   * @return the value of the named measure
   * @exception IllegalArgumentException if the named measure is not supported
   */
  public double getMeasure(String additionalMeasureName) {
    if (m_splitEvaluator instanceof AdditionalMeasureProducer) {
      return ((AdditionalMeasureProducer)m_splitEvaluator).
	getMeasure(additionalMeasureName);
    } else {
      throw new IllegalArgumentException("DedupingPRCurveCVResultProducerSplit: "
					 +"Can't return value for : "+additionalMeasureName
					 +". "+m_splitEvaluator.getClass().getName()+" "
					 +"is not an AdditionalMeasureProducer");
    }
  }
  
  /**
   * Gets a Double representing the current date and time.
   * eg: 1:46pm on 20/5/1999 -> 19990520.1346
   *
   * @return a value of type Double
   */
  public static Double getTimestamp() {

    Calendar now = Calendar.getInstance(TimeZone.getTimeZone("UTC"));
    double timestamp = now.get(Calendar.YEAR) * 10000
      + (now.get(Calendar.MONTH) + 1) * 100
      + now.get(Calendar.DAY_OF_MONTH)
      + now.get(Calendar.HOUR_OF_DAY) / 100.0
      + now.get(Calendar.MINUTE) / 10000.0;
    return new Double(timestamp);
  }
  
  /**
   * Prepare to generate results.
   *
   * @exception Exception if an error occurs during preprocessing.
   */
  public void preProcess() throws Exception {

    if (m_splitEvaluator == null) {
      throw new Exception("No SplitEvalutor set");
    }
    if (m_resultListener == null) {
      throw new Exception("No ResultListener set");
    }
    m_resultListener.preProcess(this);
  }
  
  /**
   * Perform any postprocessing. When this method is called, it indicates
   * that no more requests to generate results for the current experiment
   * will be sent.
   *
   * @exception Exception if an error occurs
   */
  public void postProcess() throws Exception {
    m_resultListener.postProcess(this);
    if (m_debugOutput) {
      if (m_zipDest != null) {
	m_zipDest.finished();
	m_zipDest = null;
      }
    }
  }
  
  /**
   * Gets the keys for a specified run number. Different run
   * numbers correspond to different randomizations of the data. Keys
   * produced should be sent to the current ResultListener
   *
   * @param run the run number to get keys for.
   * @exception Exception if a problem occurs while getting the keys
   */
  public void doRunKeys(int run) throws Exception {
    int numExtraKeys = 4;

    if (m_instances == null) {
      throw new Exception("No Instances set");
    }
    if (m_resultListener == null) {
      throw new Exception("No ResultListener set");
    }
    for (int fold = 0; fold < m_numFolds; fold++) {  
      int pointNum = 0;
      // For each subsample size
      for (int i = 0; i < m_plotPoints.length; i++) {
	// Add in some fields to the key like run and fold number, dataset name
	Object [] seKey = m_splitEvaluator.getKey();
		Object [] key = new Object [seKey.length + numExtraKeys];
	key[0] = Utils.backQuoteChars(m_instances.relationName());
	key[1] = "" + run;
	key[2] = "" + (fold + 1);
	key[3] = "" + m_plotPoints[i];
	System.arraycopy(seKey, 0, key, numExtraKeys, seKey.length);
	if (m_resultListener.isResultRequired(this, key)) {
	  try {
	    m_resultListener.acceptResult(this, key, null);
	  } catch (Exception ex) {
	    // Save the train and test datasets for debugging purposes?
	    throw ex;
	  }
	}
      }
    }
  }

  /**
   * Gets the results for a specified run number. Different run
   * numbers correspond to different randomizations of the data. Results
   * produced should be sent to the current ResultListener
   *
   * @param run the run number to get results for.
   * @exception Exception if a problem occurs while getting the results
   */
  public void doRun(int run) throws Exception {
    int numExtraKeys = 4;

    if (getRawOutput()) {
      if (m_zipDest == null) {
	m_zipDest = new OutputZipper(m_outputFile);
      }
    }
    if (m_instances == null) {
      throw new Exception("No Instances set");
    }
    if (m_resultListener == null) {
      throw new Exception("No ResultListener set");
    }
    if (!m_instances.classAttribute().isNominal()) {
      throw new Exception("Class attribute must be nominal - it is the true Object ID");
    }
    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_instances);
    runInstances.randomize(new Random(run));
    ArrayList foldList = createFoldList(runInstances, m_numFolds);

    // If a separate training file is used, create separate folds for it
    ArrayList sepFoldList = null;
    if (m_separateTrainingFile.length() > 0) {
      Instances trainInstances = new Instances(new BufferedReader(new FileReader(m_separateTrainingFile)));
      trainInstances.setClassIndex(trainInstances.numAttributes() - 1);
      trainInstances.randomize(new Random(run));
      sepFoldList = createFoldList(trainInstances, m_numFolds);
      System.out.println("Got separate training file " + m_separateTrainingFile + " of " + trainInstances.numInstances() + " instances");
    } 

    for (int fold = 0; fold < m_numFolds; fold++) {
      Instances train = ((sepFoldList == null) ?
	getTrainingFold(foldList, fold) :
	getTrainingFold(sepFoldList, fold));
      // Randomly shuffle the  training set for fold creation
      train.randomize(new Random(fold));	    

      Instances test = (Instances) foldList.get(fold);
      System.out.println("Run:" + run + " Fold:" + fold + " TestSize=" + test.numInstances());
      Object[] prResults = m_splitEvaluator.getResult(train, test);
      
      for (int i = 0; i < m_plotPoints.length; i++) {
	// Add in some fields to the key like run and fold number, dataset name
	Object [] seKey = m_splitEvaluator.getKey();
	Object [] key = new Object [seKey.length + numExtraKeys];
	key[0] = Utils.backQuoteChars(m_instances.relationName());
	key[1] = "" + run;
	key[2] = "" + (fold + 1);
	key[3] = "" + m_plotPoints[i];
	System.arraycopy(seKey, 0, key, numExtraKeys, seKey.length);
	if (m_resultListener.isResultRequired(this, key)) {
	  try {
	    Object [] seResults = processResults(prResults, m_plotPoints[i]);
	    System.out.println("Adding result:  RLevel=" + m_plotPoints[i] +
			       "\tR=" + seResults[1] + "\tP=" + seResults[2] +
			       "\tFM=" + seResults[3]); 
	    Object [] results = new Object [seResults.length + 1];
	    results[0] = getTimestamp();
	    System.arraycopy(seResults, 0, results, 1,
			     seResults.length);
	    if (m_debugOutput) {
	      String resultName = (""+run+"."+(fold+1)+"."+ "." 
				   + Utils.backQuoteChars(runInstances.relationName())
				   +"."
				   +m_splitEvaluator.toString()).replace(' ','_');
	      resultName = Utils.removeSubstring(resultName,