noisecurvecrossvalidationresultproducer.java

来自「wekaUT是 university texas austin 开发的基于wek」· Java 代码 · 共 1,346 行 · 第 1/3 页

/*
 *    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.
 */

/*
 *    NoiseCurveCrossValidationResultProducer.java
 *    
 *    Project
 *    CS 391L Machine Learning
 *
 *    Nishit Shah ([e-mail])
 *    
 *
 */

/**
 * Notes:
 * Also Read attached README file
 * Noise will be input as PERCENT ONLY (eg: 10 20 30), No fractions supported
 * The grapher needs integer values of X axis in the learning curve
 * We Take Full Dataset for the runs
 * Use the 4th Key of Fraction as the Noise_Key -- Used Key_Noise_level
 * When we add Noise to a Feature, it does not include the Class attribute
 */

package weka.experiment;
import java.util.*;
import java.io.*;
import weka.experiment.*;
import weka.core.*;
import javax.swing.JComboBox;
import javax.swing.ComboBoxModel;
import javax.swing.DefaultComboBoxModel;


/**
 * Does a N-fold cross-validation, but generates a Noise Curve
 * by also varying the number amount of Noise. Always uses the 
 * same N-fold test set for testing.
 * 
 *
 * @@author Raymond J. Mooney (mooney@@cs.utexas.edu)
 * Changed to plot Noise Curves
 */

public class NoiseCurveCrossValidationResultProducer 
    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;
    
    /** Add noise to Class Labels in Training Set */
    protected boolean m_classNoise = true;
    
    /** Add noise to Features, do not include Class as a Feature in Training Set */
    protected boolean m_featureNoise = true;
    
    /** Set features missing, do not include Class as a Feature in Training Set */
    protected boolean m_featureMiss = true;

    /** Add noise to Class Labels in Testing Set */    
    protected boolean m_classNoiseTest = true;

    /** Add noise to Features, do not include Class as a Feature in Testing Set */    
    protected boolean m_featureNoiseTest = true;

    /** Set features missing, do not include Class as a Feature in Testing Set */    
    protected boolean m_featureMissTest = true;
    
    /** 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;

    /** Store Statistics of Attributes */
    protected Vector m_AttributeStats = null;

    /** The specific points to plot, either integers representing specific numbers of training examples,
     * or decimal fractions representing percentages of the full training set -- ONLY INTEGERS SUPPORTED*/
    protected double[] m_PlotPoints;

    /** Dataset size for the runs, we take the full dataset*/
    protected int m_CurrentSize = 0;

    /** Random Number, used for randomization in each run*/
    protected Random m_Random = new Random(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 NOISE_FIELD_NAME = "Noise_levels";

    /**
     * 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 noise-curve cross validation run using a supplied "
	    +"split evaluator. Trains on different amounts of noise in the Dataset, "
	    +"repeatedly testing on the test set for that split after training.";
    }
    
    /**
     * 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("NoiseCurveCrossValidationResultProducer: 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("NoiseCurveCrossValidationResultProducer: "
					       +"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;
	numExtraKeys = 5;
	if (m_Instances == null) {
	    throw new Exception("No Instances set");
	}
	if (m_ResultListener == null) {
	    throw new Exception("No ResultListener set");
	}
	if (m_PlotPoints  == null) {
	    throw new Exception("Enter atleast one point on Noise Curve");
	}
	for(int noiseLevel = 0; noiseLevel < m_PlotPoints.length; noiseLevel++)
	    {
		for (int fold = 0; fold < m_NumFolds; fold++) {
		    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;
		    key[4] = "" + (int) m_PlotPoints[noiseLevel]; //Converting to Integer for Grapher
		    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;
			}
		    }
		}//for each fold
	    }//for each noise level
    }
    
    /** 
     * Get the maximum size of the training set based on 
     * maximum training set size from the n-fold CV 
     */
    protected int maxTrainSize() {
	return (int)(m_Instances.numInstances()*(1 - 1/((double)m_NumFolds)));
    }

    /**
     * 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;
	numExtraKeys = 5;
	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");
	}
	// Check if PlotPoint is Null
	if (m_PlotPoints == null) {
	    throw new Exception("Enter atleast one point on Noise Curve");
	}
	m_AttributeStats = new Vector(m_Instances.numAttributes());	
	//Storing both Nominal and  Numeric attributes, we use Numeric values for finding Mean and Variance
	for (int i = 0; i<m_Instances.numAttributes(); i++)
	    {
		if(m_Instances.attribute(i).isNominal()){
		int []nomCounts = (m_Instances.attributeStats(i)).nominalCounts;
		double []counts = new double[nomCounts.length];
		double []stats = new double[counts.length - 1];
		stats[0] = counts[0];
		//Calculate cumulative probabilities
		for(int j=1; j<stats.length; j++)
		    stats[j] = stats[j-1] + counts[j];
		m_AttributeStats.add(i,stats);
		}
		if(m_Instances.attribute(i).isNumeric())
		  {
			double []stats = new double[2];
			stats[0] = m_Instances.meanOrMode(i);
			stats[1] = Math.sqrt(m_Instances.variance(i));
			m_AttributeStats.add(i, stats);
			  }
	    }
	//Initialize Random Number, The experiment will be repeatable for the same run number
	m_Random = new Random(run);
	// 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 Each Noise Level
	for (int noiseLevel = 0; noiseLevel < m_PlotPoints.length; noiseLevel++)
	    {
		System.out.println("\n\nRun : " + run + " Number of Noise Levels : " + m_PlotPoints.length + " Noise Level : " + m_PlotPoints[noiseLevel] + "\n");
		for (int fold = 0; fold < m_NumFolds; fold++) {
		    Instances train = runInstances.trainCV(m_NumFolds, fold);
		    // Randomly shuffle stratified training set for fold: added by Sugato
		    train.randomize(new Random(fold));	    
		    Instances test = runInstances.testCV(m_NumFolds, fold);
		    if (m_classNoise == true){
			addClassNoise(train, test, noiseLevel);
			//System.out.println("Hi");
		    }
		    // Check m_featureNoise, if true call addFeatureNoise(train, test)
		    if (m_featureNoise == true){
			addFeatureNoise(train, test, noiseLevel);
			//System.out.println("Hi");
		    }
		    // Check m_featureMiss, if true call addFeatureMiss(train, test)
		    if (m_featureMiss == true){
			addFeatureMiss(train, test, noiseLevel);
			//System.out.println("Hi");
		    }
		    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;
		    key[4] = "" + (int)m_PlotPoints[noiseLevel];
		    System.arraycopy(seKey, 0, key, numExtraKeys, seKey.length);
		    if (m_ResultListener.isResultRequired(this, key)) {
			try {
			    System.out.println("Run:" + run + " Fold:" + fold + " Size:" + m_CurrentSize + " Noise Level:" + m_PlotPoints[noiseLevel]);
			    Instances trainSubset = new Instances(train, 0, m_CurrentSize);
			    Object [] seResults = m_SplitEvaluator.getResult(trainSubset, 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)+"."+ m_CurrentSize + "." 
						     + 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;
			}
		    }
		}//Number of Folds
	    }//For each Noise Level
    }

    /** Return the amount of noise for the ith point on the
     * curve for plotPoints as specified. Percent of NOISE Returned
     * Can Simplify this procedure to return m_PlotPoints[i] directly