crossvalidationresultproducer.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.
 */

/*
 *    CrossValidationResultProducer.java
 *    Copyright (C) 1999 Len Trigg
 *
 */


package weka.experiment;

import java.util.Enumeration;
import java.util.Calendar;
import java.util.TimeZone;
import java.util.Random;
import java.util.Vector;
import java.io.File;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.Option;
import weka.core.Utils;
import weka.core.AdditionalMeasureProducer;


/**
 * Generates for each run, carries out an n-fold cross-validation,
 * using the set SplitEvaluator to generate some results. If the class
 * attribute is nominal, the dataset is stratified. Results for each fold
 * are generated, so you may wish to use this in addition with an
 * AveragingResultProducer to obtain averages for each run.
 *
 * @author Len Trigg ([e-mail])
 * @version $Revision: 1.1.1.1 $
 */
public class CrossValidationResultProducer 
  implements ResultProducer, OptionHandler, AdditionalMeasureProducer {
  
  /** The dataset of interest */
  protected Instances m_Instances;

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

  /** The number of folds in the cross-validation */
  protected int m_NumFolds = 10;

  /** 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 SplitEvaluator used to generate results */
  protected SplitEvaluator m_SplitEvaluator = new ClassifierSplitEvaluator();

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

  /* 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";

  /**
   * 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 cross validation run using a supplied "
      +"evaluator.";
  }

  /**
   * 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("CrossValidationResultProducer: 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("CrossValidationResultProducer: "
			  +"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 {
    if (m_Instances == null) {
      throw new Exception("No Instances set");
    }
    /*    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    runInstances.randomize(new Random(run));
    if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_NumFolds);
      } */
    for (int fold = 0; fold < m_NumFolds; fold++) {
      // 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 + 3];
      key[0] = Utils.backQuoteChars(m_Instances.relationName());
      key[1] = "" + run;
      key[2] = "" + (fold + 1);
      System.arraycopy(seKey, 0, key, 3, 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 {

    if (getRawOutput()) {
      if (m_ZipDest == null) {
	m_ZipDest = new OutputZipper(m_OutputFile);
      }
    }

    if (m_Instances == null) {
      throw new Exception("No Instances set");
    }
    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    runInstances.randomize(new Random(run));
    if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_NumFolds);
    }
    for (int fold = 0; fold < m_NumFolds; fold++) {
      // 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 + 3];
      key[0] =  Utils.backQuoteChars(m_Instances.relationName());
      key[1] = "" + run;
      key[2] = "" + (fold + 1);
      System.arraycopy(seKey, 0, key, 3, seKey.length);
      if (m_ResultListener.isResultRequired(this, key)) {
	Instances train = runInstances.trainCV(m_NumFolds, fold);
	Instances test = runInstances.testCV(m_NumFolds, fold);
	try {
	  Object [] seResults = m_SplitEvaluator.getResult(train, test);
	  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, 
					       "weka.classifiers.");
	    resultName = Utils.removeSubstring(resultName, 
					       "weka.filters.");
	    resultName = Utils.removeSubstring(resultName, 
					       "weka.attributeSelection.");
	    m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName);
	  }
	  m_ResultListener.acceptResult(this, key, results);
	} catch (Exception ex) {
	  // Save the train and test datasets for debugging purposes?
	  throw ex;
	}
      }
    }
  }

  /**
   * Gets the names of each of the columns produced for a single run.
   * This method should really be static.
   *
   * @return an array containing the name of each column
   */
  public String [] getKeyNames() {

    String [] keyNames = m_SplitEvaluator.getKeyNames();
    // Add in the names of our extra key fields
    String [] newKeyNames = new String [keyNames.length + 3];
    newKeyNames[0] = DATASET_FIELD_NAME;
    newKeyNames[1] = RUN_FIELD_NAME;
    newKeyNames[2] = FOLD_FIELD_NAME;
    System.arraycopy(keyNames, 0, newKeyNames, 3, keyNames.length);
    return newKeyNames;
  }

  /**
   * Gets the data types of each of the columns produced for a single run.
   * This method should really be static.
   *
   * @return an array containing objects of the type of each column. The 
   * objects should be Strings, or Doubles.
   */
  public Object [] getKeyTypes() {

    Object [] keyTypes = m_SplitEvaluator.getKeyTypes();
    // Add in the types of our extra fields
    Object [] newKeyTypes = new String [keyTypes.length + 3];
    newKeyTypes[0] = new String();