/*
* 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.
*/
/*
* BudgetedLearningCurveResultProducer.java
* Copyright (C) 2005 Prem Melville
*
*/
////////////////////////////////
//
// WARNING: UNDER DEVELOPMENT
//
////////////////////////////////
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 (melville@cs.utexas.edu)
*/
public class BudgetedLearningCurveResultProducer
implements ResultProducer, OptionHandler, AdditionalMeasureProducer {
/**
* The directory used when loading feature cost files on demand, null indicates
* current directory
*/
protected File m_FeatureCostDirectory = new File(System.getProperty("user.dir")+"/featureCosts");
/** Costs for acquiring each feature */
protected double []m_FeatureCosts = null;
/** 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();
protected SplitEvaluator m_SplitEvaluator = new FeatureCostSensitiveClassifierSplitEvaluator();
/** 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 double m_LowerSize = 0;
/**
* The maximum number of instances to use. -1 indicates no maximum
* (other than the total number of instances)
*/
protected double m_UpperSize = -1;
/** The number of instances to add at each step */
protected double 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;
/** Cost to build current model */
protected double m_Cost = 0.0;
/* 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 result field containing the sample selection time */
public static String SELECTION_TIME_FIELD_NAME = "Selection_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";
public static String COST_FIELD_NAME = "Model_cost";
/* Extension for feature cost files */
public static String FEATURE_COST_FILE_EXTENSION = ".featureCosts";
/* 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.";
}
/**
* Sets the dataset that results will be obtained for.
*
* @param instances a value of type 'Instances'.
*/
public void setInstances(Instances instances) {
m_Instances = instances;
loadFeatureCosts();
}
/**
* Returns the directory that will be searched for feature cost files when
* loading on demand.
*
* @return The cost file search directory.
*/
public File getFeatureCostDirectory() {
return m_FeatureCostDirectory;
}
/**
* Sets the directory that will be searched for feature cost files when
* loading on demand.
*
* @param newDir The cost file search directory.
*/
public void setFeatureCostDirectory(File newDir) {
if (newDir.isDirectory()) {
m_FeatureCostDirectory = newDir;
} else {
m_FeatureCostDirectory = new File(newDir.getParent());
}
}
//Read feature costs from file
protected void loadFeatureCosts(){
try{
String costName = m_Instances.relationName() + FEATURE_COST_FILE_EXTENSION;
File costFile = new File(getFeatureCostDirectory(), costName);
if (!costFile.exists()) {
throw new Exception("Feature cost file doesn't exist: " + costFile);
}
int numFeatures = numFeatures();
m_FeatureCosts = new double[numFeatures];
BufferedReader bin = new BufferedReader(new FileReader(costFile));
String line = bin.readLine();//read first line - ignore rest of file
bin.close();
StringTokenizer st = new StringTokenizer(line);
int ctr = 0;//count number of costs in the file
while(st.hasMoreTokens()) {
if(ctr < numFeatures){
m_FeatureCosts[ctr] = Double.parseDouble(st.nextToken());
ctr++;
}else{
//throw new Exception("Feature cost file has too many costs.");
System.err.println("Feature cost file has too many costs: "+costFile);
System.exit(0);
}
}
if(ctr < numFeatures){
System.err.println("Feature cost file has too few costs: "+costFile);
System.exit(0);
//throw new Exception("Feature cost file has too few costs.");
}
}catch (Exception e){
System.err.println (e);
}
//DEBUG
Utils.println(m_FeatureCosts);
}
/**
* 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 = stepSize(m_StepSize);
} else {
m_CurrentSize = lowerSize(m_LowerSize);
}
while (m_CurrentSize <= maxNumQueries()) {
// 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 += stepSize(m_StepSize);
}
}
}
}
/**
* Get the maximum number of queries base on the upperSize limit
* or maximum training set size from the n-fold CV
*/
protected int maxNumQueries() {
if (m_UpperSize == -1 || m_PlotPoints != null)
return ((int)(m_Instances.numInstances()*(1 - 1/((double)m_NumFolds))))*numFeatures();
else if(isInteger(m_UpperSize))
return (int) m_UpperSize;
else return (int) (m_UpperSize*((int)(m_Instances.numInstances()*(1 - 1/((double)m_NumFolds))))*numFeatures());
}
//If lowersize is a fraction, compute as a fraction of total number of features.
protected int lowerSize(double lowerSize){
if(isInteger(lowerSize))
return (int) lowerSize;
else return (int) (lowerSize * ((int)(m_Instances.numInstances()*(1 - 1/((double)m_NumFolds))))*numFeatures());
}
//If lowersize is a fraction, compute as a fraction of total number of features.
protected int stepSize(double stepSize){
int out;
if(isInteger(stepSize))
out = (int) stepSize;
else out = (int) (stepSize * ((int)(m_Instances.numInstances()*(1 - 1/((double)m_NumFolds))))*numFeatures());
//step size must at least be 1
if(out<1) out=1;
return out;
}
//Returns the number of features in the datasets (excluding the class attribute)
protected int numFeatures(){
return (m_Instances.numAttributes() - 1);
}
/**
* 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");
}
//Make local copy of data
//Initialize dataset - based on initial ablation level
//Run classifier
//Get queries
//Update dataset with result of query
if(m_SplitEvaluator instanceof FeatureCostSensitiveClassifierSplitEvaluator){
((FeatureCostSensitiveClassifierSplitEvaluator)m_SplitEvaluator).setFeatureCosts(m_FeatureCosts);
}
int numFeatures = numFeatures();
// 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
m_Cost = 0.0;//initialize cost counter
Instances fullTrain = runInstances.trainCV(m_NumFolds, fold);
// Randomly shuffle stratified training set for fold
fullTrain.randomize(new Random(fold));
Instances train = initializeData(fullTrain);
boolean [][]queryMatrix = new boolean[train.numInstances()][numFeatures];
//initially queryMatrix is set to all false
boolean firstPoint = true;
int prevSize = 0;
Instances test = runInstances.testCV(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 = stepSize(m_StepSize);
} else {
m_CurrentSize = lowerSize(m_LowerSize);
}
//maxQueries should not exceed total number of queries possible in current fold
int maxQueries = maxNumQueries();
int trainSize = fullTrain.numInstances()*numFeatures();
if(maxQueries > trainSize) maxQueries = trainSize;
//System.out.println(train);
while (m_CurrentSize <= maxQueries) {
long selectionTimeStart, selectionTimeElapsed;
// 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 {
if(m_IsFraction)
System.out.println("Run:" + run + " Fold:" + fold + " Size:" + m_CurrentSize + " Fraction:" + m_PlotPoints[pointNum]);
else
System.out.println("Run:" + run + " Fold:" + fold + " Size:" + m_CurrentSize);
if(firstPoint){//the first training set is always randomly selected
firstPoint = false;
selectionTimeStart = System.currentTimeMillis();
makeRandomQueries(train,fullTrain,m_CurrentSize - prevSize,queryMatrix,run*1000+fold);
selectionTimeElapsed = System.currentTimeMillis() - selectionTimeStart;
}else{
//use current classifier to actively select instance-feature pairs
//acquire features-values and add to the training set
selectionTimeStart = System.currentTimeMillis();
makeQueries(train,fullTrain,m_CurrentSize - prevSize,queryMatrix,run*1000+fold);
selectionTimeElapsed = System.currentTimeMillis() - selectionTimeStart;
}
Object [] seResults = m_SplitEvaluator.getResult(train, test);
Object [] results = new Object [seResults.length + 3];
results[0] = getTimestamp();
results[1] = new Double(selectionTimeElapsed / 1000.0);
results[2] = new Double(m_Cost);
System.arraycopy(seResults, 0, results, 3,
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;
}
}
prevSize = m_CurrentSize;
if (m_PlotPoints != null) {
pointNum ++;
m_CurrentSize = plotPoint(pointNum);
}
else {
m_CurrentSize += stepSize(m_StepSize);
}
//System.out.println(train);
}
}
}
//Create initial training set with only the class labels
protected Instances initializeData(Instances fulltrain){
int numInstances = fulltrain.numInstances();
int numAtts = fulltrain.numAttributes();
int classIndex = fulltrain.classIndex();
Instances train = new Instances(fulltrain, numInstances);
for(int i=0; i<numInstances; i++){
Instance newInst = new Instance(numAtts);
newInst.setValue(classIndex,(fulltrain.instance(i)).classValue());
train.add(newInst);
}
return train;
}
/**
* Use current classifier to actively select specified number of instances-feature queries
* to be made. Update training set and the query matrix
*
* @param train instances with missing feature-values
* @param fulltrain instances with all feature-values
* @param num number of queries to make
* @param queryMatrix matrix to track available queries
* @param seed random seed needed if the learner is not a BudgetedLearner
*/
protected void makeQueries(Instances train, Instances fulltrain, int num, boolean [][]queryMatrix, int seed) throws Exception{
Classifier classifier;
try{
classifier = ((ClassifierSplitEvaluator)m_SplitEvaluator).getClassifier();
}catch (Exception ex){
throw new Exception("Budgeted learning is only implemented for evaluators of classifiers.");
}
if(classifier instanceof BudgetedLearner){
//get instance-feature pairs picked by the classifier
Pair []queries = ((BudgetedLearner)classifier).selectInstancesForFeatures(train, num, queryMatrix);
if(queries.length!=num) throw new Exception("Incorrect number of queries made!");
transferQueries(train, fulltrain, queries, queryMatrix);
}else{//randomly pick examples from local pool
makeRandomQueries(train, fulltrain, num, queryMatrix, seed);
}
}
//Randomly select queries to make
protected void makeRandomQueries(Instances train, Instances fulltrain, int num, boolean [][]queryMatrix, int seed) throws Exception{
int numInstances = fulltrain.numInstances();
int numFeatures = numFeatures();
//create a list of query pairs
ArrayList allQueries = new ArrayList();
for(int i=0; i<numInstances; i++)
for(int j=0; j<numFeatures; j++)
if(!queryMatrix[i][j]) allQueries.add(new Pair(i,j));
//randomly select num queries
Pair []queries = new Pair[num];
Random random = new Random(seed);
System.out.println("Making random queries ...");
System.out.print(allQueries.size()+" - "+num+" = ");
for(int i=0; i<num; i++){
int index = random.nextInt(allQueries.size());
queries[i] = (Pair) allQueries.get(index);
allQueries.remove(index);
}
System.out.println(allQueries.size());
transferQueries(train, fulltrain, queries, queryMatrix);
}
//Transfer requested feature-value to training set
protected void transferQueries(Instances train, Instances fulltrain, Pair []queries, boolean [][]queryMatrix){
int instanceIndex, featureIndex;
for(int i=0; i<queries.length; i++){
instanceIndex = (int) queries[i].first;
featureIndex = (int) queries[i].second;
if(queryMatrix[instanceIndex][featureIndex]){
System.err.println("Query tracking failure!");
}else{
(train.instance(instanceIndex)).setValue(featureIndex,(fulltrain.instance(instanceIndex)).value(featureIndex));
m_Cost += m_FeatureCosts[featureIndex];
System.out.println("Query for ("+instanceIndex+","+featureIndex+") = "+ (train.instance(instanceIndex)).value(featureIndex) + "\tCost = "+m_FeatureCosts[featureIndex]+"\tTotalCost = "+m_Cost);
queryMatrix[instanceIndex][featureIndex] = true;
}
}
}
/**
* Add new instances to the given set of instances.
*
* @param data given instances
* @param newData set of instances to add to given instances
*/
protected void addInstances(Instances data, Instances newData){
for(int i=0; i<newData.numInstances(); i++)
data.add(newData.instance(i));
}
/** Determines if the points specified are fractions of the total number of examples */
protected boolean setIsFraction(){
if (m_PlotPoints != null){
if(!isInteger(m_PlotPoints[0]))//if the first point is not an integer
m_IsFraction = true;
else
m_IsFraction = false;
}// else{
// if(!isInteger(m_StepSize))//if the step size is not an integer
// m_IsFraction = true;
// else
// m_IsFraction = false;
// }
return m_IsFraction;
}
/** Return the number of instance-feature queries for the ith point on the
* curve for plotPoints as specified.
*/
protected int plotPoint(int i) {
// If i beyond number of given plot points return a value greater than maximum queries
if (i >= m_PlotPoints.length)
return maxNumQueries() + 1;
double point = m_PlotPoints[i];
// If plot point is an integer (other than a non-initial 1)
// treat it as a specific number of queries
if (isInteger(point) && !(Utils.eq(point, 1.0) && i!=0))
return (int)point;
else
// Otherwise, treat it as a percentage of the full set
return (int)Math.round(point * maxNumQueries());
}
/** Return true if the given double represents an integer value */
protected static boolean isInteger(double val) {
return Utils.eq(Math.floor(val), Math.ceil(val));
}
/**
* 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
int numExtraKeys;
if(m_IsFraction)
numExtraKeys = 5;
else numExtraKeys = 4;
String [] newKeyNames = new String [keyNames.length + numExtraKeys];
newKeyNames[0] = DATASET_FIELD_NAME;
newKeyNames[1] = RUN_FIELD_NAME;
newKeyNames[2] = FOLD_FIELD_NAME;
newKeyNames[3] = STEP_FIELD_NAME;
if(m_IsFraction) newKeyNames[4] = FRACTION_FIELD_NAME;
System.arraycopy(keyNames, 0, newKeyNames, numExtraKeys, 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();
int numExtraKeys;
if(m_IsFraction)
numExtraKeys = 5;
else numExtraKeys = 4;
// Add in the types of our extra fields
Object [] newKeyTypes = new String [keyTypes.length + numExtraKeys];
newKeyTypes[0] = new String();
newKeyTypes[1] = new String();
newKeyTypes[2] = new String();
newKeyTypes[3] = new String();
if(m_IsFraction) newKeyTypes[4] = new String();
System.arraycopy(keyTypes, 0, newKeyTypes, numExtraKeys, keyTypes.length);
return newKeyTypes;
}
/**
* 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 [] getResultNames() {
String [] resultNames = m_SplitEvaluator.getResultNames();
// Add in the names of our extra Result fields
String [] newResultNames = new String [resultNames.length + 3];
newResultNames[0] = TIMESTAMP_FIELD_NAME;
newResultNames[1] = SELECTION_TIME_FIELD_NAME;
newResultNames[2] = COST_FIELD_NAME;
System.arraycopy(resultNames, 0, newResultNames, 3, resultNames.length);
return newResultNames;
}
/**
* 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 [] getResultTypes() {
Object [] resultTypes = m_SplitEvaluator.getResultTypes();
// Add in the types of our extra Result fields
Object [] newResultTypes = new Object [resultTypes.length + 3];
newResultTypes[0] = new Double(0);
newResultTypes[1] = new Double(0);
newResultTypes[2] = new Double(0);
System.arraycopy(resultTypes, 0, newResultTypes, 3, resultTypes.length);
return newResultTypes;
}
/**
* Gets a description of the internal settings of the result
* producer, sufficient for distinguishing a ResultProducer
* instance from another with different settings (ignoring
* those settings set through this interface). For example,
* a cross-validation ResultProducer may have a setting for the
* number of folds. For a given state, the results produced should
* be compatible. Typically if a ResultProducer is an OptionHandler,
* this string will represent the command line arguments required
* to set the ResultProducer to that state.
*
* @return the description of the ResultProducer state, or null
* if no state is defined
*/
public String getCompatibilityState() {
String result = "-X " + m_NumFolds + " -S " + getStepSize() +
" -L " + getLowerSize() + " -U " + getUpperSize() + " ";
if (m_SplitEvaluator == null) {
result += "<null SplitEvaluator>";
} else {
result += "-W " + m_SplitEvaluator.getClass().getName();
}
return result + " --";
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String outputFileTipText() {
return "Set the destination for saving raw output. If the rawOutput "
+"option is selected, then output from the splitEvaluator for "
+"individual folds is saved. If the destination is a directory, "
+"then each output is saved to an individual gzip file; if the "
+"destination is a file, then each output is saved as an entry "
+"in a zip file.";
}
/**
* Get the value of OutputFile.
*
* @return Value of OutputFile.
*/
public File getOutputFile() {
return m_OutputFile;
}
/**
* Set the value of OutputFile.
*
* @param newOutputFile Value to assign to OutputFile.
*/
public void setOutputFile(File newOutputFile) {
m_OutputFile = newOutputFile;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numFoldsTipText() {
return "Number of folds to use in cross validation.";
}
/**
* Get the value of NumFolds.
*
* @return Value of NumFolds.
*/
public int getNumFolds() {
return m_NumFolds;
}
/**
* Set the value of NumFolds.
*
* @param newNumFolds Value to assign to NumFolds.
*/
public void setNumFolds(int newNumFolds) {
m_NumFolds = newNumFolds;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String lowerSizeTipText() {
return "Set the minimum number of instances in a training set. Setting zero "
+ "here will actually use <stepSize> number of instances at the first "
+ "step (since performance at zero instances is predictable)";
}
/**
* Get the value of LowerSize.
*
* @return Value of LowerSize.
*/
public double getLowerSize() {
return m_LowerSize;
}
/**
* Set the value of LowerSize.
*
* @param newLowerSize Value to assign to
* LowerSize.
*/
public void setLowerSize(double newLowerSize) {
m_LowerSize = newLowerSize;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String upperSizeTipText() {
return "Set the maximum number of instances in a training set. Setting -1 "
+ "sets no upper limit (other than the total number of instances "
+ "in the full training set)";
}
/**
* Get the value of UpperSize.
*
* @return Value of UpperSize.
*/
public double getUpperSize() {
return m_UpperSize;
}
/**
* Set the value of UpperSize.
*
* @param newUpperSize Value to assign to
* UpperSize.
*/
public void setUpperSize(double newUpperSize) {
m_UpperSize = newUpperSize;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String stepSizeTipText() {
return "Set the number of instances to add to the training data at each step.";
}
/**
* Get the value of StepSize.
*
* @return Value of StepSize.
*/
public double getStepSize() {
return m_StepSize;
}
/**
* Set the value of StepSize.
*
* @param newStepSize Value to assign to
* StepSize.
*/
public void setStepSize(double newStepSize) {
m_StepSize = newStepSize;
//setIsFraction();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String plotPointsTipText() {
return "A list of specific points to plot as a string of numbers separated by commas or spaces. "+
"Whole numbers indicate a specific number of examples, "+
"decimal fractions indicate a fraction of the total training set. "+
"Specifying plot points overrides step size, lower size, and upper size parameters.";
}
/**
* Get the value of PlotPoints.
*
* @return Value of PlotPoints.
*/
public String getPlotPoints() {
StringBuffer buf = new StringBuffer();
if (m_PlotPoints != null)
for (int i=0; i < m_PlotPoints.length; i++) {
buf.append(m_PlotPoints[i]);
if (i != (m_PlotPoints.length -1))
buf.append(" ");
}
return buf.toString();
}
/**
* Set the value of PlotPoints.
*
* @param plotPoints Value to assign to
* PlotPoints.
*/
public void setPlotPoints(String plotPoints) {
m_PlotPoints = parsePlotPoints(plotPoints);
setIsFraction();
}
/**
* Parse a string of doubles separated by commas or spaces into a sorted array of doubles
*/
protected double[] parsePlotPoints(String plotPoints) {
StringTokenizer tokenizer = new StringTokenizer(plotPoints," ,\t");
double[] result = null;
int count = tokenizer.countTokens();
if (count > 0)
result = new double[count];
else
return null;
int i = 0;
while(tokenizer.hasMoreTokens()) {
result[i] = Double.parseDouble(tokenizer.nextToken());
i++;
}
Arrays.sort(result);
return result;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String rawOutputTipText() {
return "Save raw output (useful for debugging). If set, then output is "
+"sent to the destination specified by outputFile";
}
/**
* Get if raw split evaluator output is to be saved
* @return true if raw split evalutor output is to be saved
*/
public boolean getRawOutput() {
return m_debugOutput;
}
/**
* Set to true if raw split evaluator output is to be saved
* @param d true if output is to be saved
*/
public void setRawOutput(boolean d) {
m_debugOutput = d;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String splitEvaluatorTipText() {
return "The evaluator to apply to the cross validation folds. "
+"This may be a classifier, regression scheme etc.";
}
/**
* Get the SplitEvaluator.
*
* @return the SplitEvaluator.
*/
public SplitEvaluator getSplitEvaluator() {
return m_SplitEvaluator;
}
/**
* Set the SplitEvaluator.
*
* @param newSplitEvaluator new SplitEvaluator to use.
*/
public void setSplitEvaluator(SplitEvaluator newSplitEvaluator) {
m_SplitEvaluator = newSplitEvaluator;
m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(8);
newVector.addElement(new Option(
"\tThe number of folds to use for the cross-validation.\n"
+"\t(default 10)",
"X", 1,
"-X <number of folds>"));
newVector.addElement(new Option(
"\tThe number of instances to add at each step on the learning curve.",
"S", 1,
"-S <step size>"));
newVector.addElement(new Option(
"\tThe minmum number of instances in a training set. Setting zero"
+ "\there will actually use <stepSize> number of instances at the first"
+ "\tstep (since performance at zero instances is predictable)",
"L", 1,
"-L <lower bound>"));
newVector.addElement(new Option(
"\tThe maximum number of instances in a training set. Setting -1 "
+ "\tsets no upper limit (other than the total number of instances "
+ "\tin the full training set)",
"U", 1,
"-U <upper bound>"));
newVector.addElement(new Option(
"\tA list of specific points to plot as a string of numbers\n"+
"separated by commas or spaces.\n"+
"Whole numbers indicate a specific number of examples,\n"+
"decimal fractions indicate a fraction of the total training set.\n"+
"Specifying plot points overrides the S, L, and U parameters",
"P", 1,
"-P <point list>"));
newVector.addElement(new Option(
"Save raw split evaluator output.",
"D",0,"-D"));
newVector.addElement(new Option(
"\tThe filename where raw output will be stored.\n"
+"\tIf a directory name is specified then then individual\n"
+"\toutputs will be gzipped, otherwise all output will be\n"
+"\tzipped to the named file. Use in conjuction with -D."
+"\t(default splitEvalutorOut.zip)",
"O", 1,
"-O <file/directory name/path>"));
newVector.addElement(new Option(
"\tThe full class name of a SplitEvaluator.\n"
+"\teg: weka.experiment.ClassifierSplitEvaluator",
"W", 1,
"-W <class name>"));
if ((m_SplitEvaluator != null) &&
(m_SplitEvaluator instanceof OptionHandler)) {
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to split evaluator "
+ m_SplitEvaluator.getClass().getName() + ":"));
Enumeration enum = ((OptionHandler)m_SplitEvaluator).listOptions();
while (enum.hasMoreElements()) {
newVector.addElement(enum.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -X num_folds <br>
* The number of folds to use for the cross-validation. <p>
*
* -D <br>
* Specify that raw split evaluator output is to be saved. <p>
*
* -O file/directory name <br>
* Specify the file or directory to which raw split evaluator output
* is to be saved. If a directory is specified, then each output string
* is saved as an individual gzip file. If a file is specified, then
* each output string is saved as an entry in a zip file. <p>
*
* -W classname <br>
* Specify the full class name of the split evaluator. <p>
*
* All option after -- will be passed to the split evaluator.
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
setRawOutput(Utils.getFlag('D', options));
String featureDir = Utils.getOption('F', options);
if (featureDir.length() != 0) {
setFeatureCostDirectory(new File(featureDir));
}
String fName = Utils.getOption('O', options);
if (fName.length() != 0) {
setOutputFile(new File(fName));
}
String numFolds = Utils.getOption('X', options);
if (numFolds.length() != 0) {
setNumFolds(Integer.parseInt(numFolds));
} else {
setNumFolds(10);
}
String stepSize = Utils.getOption('S', options);
if (stepSize.length() != 0) {
setStepSize(Double.parseDouble(stepSize));
} else {
setStepSize(10);
}
String lowerSize = Utils.getOption('L', options);
if (lowerSize.length() != 0) {
setLowerSize(Double.parseDouble(lowerSize));
} else {
setLowerSize(0);
}
String upperSize = Utils.getOption('U', options);
if (upperSize.length() != 0) {
setUpperSize(Double.parseDouble(upperSize));
} else {
setUpperSize(-1);
}
String plotPoints = Utils.getOption('P', options);
if (plotPoints.length() != 0) {
setPlotPoints(plotPoints);
} else {
setPlotPoints("");
}
String seName = Utils.getOption('W', options);
if (seName.length() == 0) {
throw new Exception("A SplitEvaluator must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// SE.
setSplitEvaluator((SplitEvaluator)Utils.forName(
SplitEvaluator.class,
seName,
null));
if (getSplitEvaluator() instanceof OptionHandler) {
((OptionHandler) getSplitEvaluator())
.setOptions(Utils.partitionOptions(options));
}
}
/**
* Gets the current settings of the result producer.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] seOptions = new String [0];
if ((m_SplitEvaluator != null) &&
(m_SplitEvaluator instanceof OptionHandler)) {
seOptions = ((OptionHandler)m_SplitEvaluator).getOptions();
}
String [] options = new String [seOptions.length + 17];
int current = 0;
options[current++] = "-F"; options[current++] = "" + getFeatureCostDirectory();
options[current++] = "-X"; options[current++] = "" + getNumFolds();
if (getRawOutput()) {
options[current++] = "-D";
}
options[current++] = "-O";
options[current++] = getOutputFile().getName();
options[current++] = "-S";
options[current++] = "" + getStepSize();
options[current++] = "-L";
options[current++] = "" + getLowerSize();
options[current++] = "-U";
options[current++] = "" + getUpperSize();
options[current++] = "-P";
options[current++] = getPlotPoints();
if (getSplitEvaluator() != null) {
options[current++] = "-W";
options[current++] = getSplitEvaluator().getClass().getName();
}
options[current++] = "--";
System.arraycopy(seOptions, 0, options, current,
seOptions.length);
current += seOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Gets a text descrption of the result producer.
*
* @return a text description of the result producer.
*/
public String toString() {
String result = "BudgetedLearningCurveResultProducer: ";
result += getCompatibilityState();
if (m_Instances == null) {
result += ": <null Instances>";
} else {
result += ": " + Utils.backQuoteChars(m_Instances.relationName());
}
return result;
}
// Quick test
public static void main(String [] args) {
BudgetedLearningCurveResultProducer rp = new BudgetedLearningCurveResultProducer();
rp.setPlotPoints(args[0]);
System.out.println(rp.getPlotPoints());
if (rp.m_PlotPoints != null) System.out.println(isInteger(rp.m_PlotPoints[0]));
}
} // BudgetedLearningCurveResultProducer