activefeatureacquisitioncvresultproducer.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.
 */

/*
 *    ActiveFeatureAcquisitionCVResultProducer.java
 *    Copyright (C) 2003 Prem Melville
 *
 */

package weka.experiment;

import java.util.*;
import java.io.*;
import weka.classifiers.*;
import weka.core.*;


/**
 * Does an N-fold cross-validation, but generates a learning curve by
 * also varying the number of training examples. Creates a split that
 * uses increasingly larger fractions of the full training set from
 * the N fold but always using the same N-fold test set for
 * testing. If this is applied to an active learner, then the training
 * examples are selected actively by the learner from the pool of
 * unlabeled examples. If this is not used with an active learner, it
 * should produce the same results as the
 * LearningCurveCrossValidationResultProducer.
 *
 * @author Prem Melville ([e-mail]) 
 */

public class ActiveFeatureAcquisitionCVResultProducer 
    implements ResultProducer, OptionHandler, AdditionalMeasureProducer {
    
    /** Indices for range of non-local features (to be acquired) */
    protected int m_NonLocalStartIndex = 15; 
    protected int m_NonLocalEndIndex = 39;
    
    /** Ablational level */
    protected double m_AblationLevel = -1;
    
    /** Select features for ablation randomly */
    protected boolean m_RandomAblation = false;
    
    /** 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 minimum number of instances to use. If this is zero, the first
     * step will contain m_StepSize instances 
     */
    protected int m_LowerSize = 0;
  
    /**
     * The maximum number of instances to use. -1 indicates no maximum 
     * (other than the total number of instances)
     */
    protected int m_UpperSize = -1;

    /** The number of instances to add at each step */
    protected int m_StepSize = 1;

    /** 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;

    /** The current dataset size during stepping */
    protected int m_CurrentSize = 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 STEP_FIELD_NAME = "Total_instances";

    /* The name of the key field containing the fraction of total instances used */
    public static String FRACTION_FIELD_NAME = "Fraction_instances";

    /* Indicates whether fractions or actual number of instances have been specified */
    protected boolean m_IsFraction = false;
    
    /**
     * 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 "
	    +"split evaluator. Trains on increasing subsets of the training data for each split, "
	    +"repeatedly testing on the test set for that split after training on subsets of various sizes.";
    }
    
    

    /**
     * Get the value of m_RandomAblation.
     * @return value of m_RandomAblation.
     */
    public boolean getRandomAblation() {
	return m_RandomAblation;
    }
    
    /**
     * Set the value of m_RandomAblation.
     * @param v  Value to assign to m_RandomAblation.
     */
    public void setRandomAblation(boolean  v) {
	m_RandomAblation = v;
    }
    

    public void setAblationLevel(double abLevel){
	m_AblationLevel = abLevel;
    }

    public double getAblationLevel(){
	return m_AblationLevel;
    }
    

    /**
     * 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("LearningCurveCrossValidationResultProducer: 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("LearningCurveCrossValidationResultProducer: "
					       +"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;
	if(m_IsFraction)
	    numExtraKeys = 5;
	else 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
	    if (m_PlotPoints != null) {
		m_CurrentSize = plotPoint(0);
	    }
	    else if (m_LowerSize == 0) {
		m_CurrentSize = m_StepSize;
	    } else {
		m_CurrentSize = m_LowerSize;
	    }
	    while (m_CurrentSize <= maxTrainSize()) {
		// 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_CurrentSize;
		if(m_IsFraction) key[4] = "" + m_PlotPoints[pointNum];
		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;
		    }
		}
		if (m_PlotPoints != null) {
		    pointNum ++;
		    m_CurrentSize = plotPoint(pointNum);
		}
		else {
		    m_CurrentSize += m_StepSize;
		}
	    }
	}
    }

    /** 
     * Get the maximum size of the training set based on  upperSize limit
     * or maximum training set size from the n-fold CV 
     */
    protected int maxTrainSize() {
	if (m_UpperSize == -1 || m_PlotPoints != null)
	    return (int)(m_Instances.numInstances()*(1 - 1/((double)m_NumFolds)));
	else return m_UpperSize;
    }

    /**
     * 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;
	if(m_IsFraction)
	    numExtraKeys = 5;
	else 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_AblationLevel>=0 && !m_RandomAblation){
	    int numAtts = m_Instances.numAttributes()-1;
	    m_NonLocalStartIndex = (int) (m_AblationLevel * numAtts);
	    if(m_NonLocalStartIndex<1) m_NonLocalStartIndex=1;//atleast one feature should be available to begin with
	    m_NonLocalEndIndex = numAtts-1;//exclude the class attribute
	    assert (numAtts==m_Instances.classIndex());
	}

	// 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++) {//For each fold
	    Instances fullTrain = runInstances.trainCV(m_NumFolds, fold);
	    // Randomly shuffle stratified training set for fold: added by Sugato
	    fullTrain.randomize(new Random(fold));
	    
	    Instances global = new Instances(fullTrain,0);//only global instances
	    Instances local = new Instances(fullTrain);//only local instance
	    HashMap localToGlobal = new HashMap();//map local instances to global instances
	    //only copy header and allocate space for training set
	    Instances train=new Instances(fullTrain,fullTrain.numInstances());
	    boolean firstPoint = true;
	    int prevSize = 0;
	    Instances test = runInstances.testCV(m_NumFolds, fold);
	    int pointNum = 0;
	    
	    ablateFeatures(local, run*100+fold);//replace non local features with missing
	    //associate local instances with global instances
	    for(int index=0; index<fullTrain.numInstances(); index++){
		localToGlobal.put(local.instance(index),fullTrain.instance(index));
	    }
	    
	    // For each subsample size
	    if (m_PlotPoints != null) {
		m_CurrentSize = plotPoint(0);
	    }
	    else if (m_LowerSize == 0) {
		m_CurrentSize = m_StepSize;
	    } else {