expab_tc2.java

用于multivariate时间序列分类

/**
 * This is a "hack" class. It's just to help me test out some ideas. 
 * I'm running out of time for this conference and I'm really trying
 * to put together something stunning in terms of performance. 
 * 
 * @author Waleed Kadous
 * @version $Id: ExpAB_TC2.java,v 1.1.1.1 2002/06/28 07:36:16 waleed Exp $
 */

package tclass;   
import tclass.util.*; 
// import tclass.learnalg.*; 
import weka.classifiers.*; 
import weka.classifiers.j48.*; 
import weka.core.*; 
import java.io.*; 


class ABClassifier {
    AdaBoostM1 dt; 
    String name = "AdaBoost"; 
    String description = "AdaBoost Classifier"; 
    public ABClassifier(AdaBoostM1 dt){
        this.dt = dt; 
    } 
      public String getName(){
	return name; 
    }

   public String getDescription(){
	return description; 
    }

    public void classify(Instance inst, ClassificationI
			 classn) throws Exception {
        double bestClass = dt.classifyInstance(inst); 
        classn.setPredictedClass((int) bestClass); 
        classn.setPredictedClassConfidence(1); 
    }
}

public class ExpAB_TC2  {
    // Ok. What we are going to do is to separate the learning task in 
    // an interesting way. 
    // First of all, though, the standard stuff
    
    String domDescFile = "sl.tdd"; 
    String trainDataFile = "sl.tsl"; 
    String testDataFile = "sl.ttl"; 
    // String globalDesc = "test._gc"; 
    // String evExtractDesc = "test._ee";
    String evClusterDesc = "test._ec"; 
    String settingsFile = "test.tal"; 
    String numDivs = "5"; 

    void parseArgs(String[] args){
        for(int i=0; i < args.length; i++){
            if(args[i].equals("-tr")){
                trainDataFile = args[++i]; 
            }
            if(args[i].equals("-te")){
                testDataFile = args[++i];
            }
            if(args[i].equals("-nd")){
                numDivs = args[++i]; 
            }
            if(args[i].equals("-settings")){
                settingsFile = args[++i]; 
            }
        }
    }
    public static void main(String[] args) throws Exception {
        Debug.setDebugLevel(Debug.PROGRESS); 
        ExpAB_TC2 thisExp = new ExpAB_TC2(); 
        thisExp.parseArgs(args); 
        DomDesc domDesc = new DomDesc(thisExp.domDescFile); 
        ClassStreamVecI trainStreamData = new
            ClassStreamVec(thisExp.trainDataFile, domDesc); 
        ClassStreamVecI testStreamData = new
            ClassStreamVec(thisExp.testDataFile, domDesc); 

        Debug.dp(Debug.PROGRESS, "PROGRESS: Data read in");  
        Settings settings = new Settings(thisExp.settingsFile, domDesc); 
        

        EventExtractor evExtractor = settings.getEventExtractor(); 
        // Global data is likely to be included in every model; so we
        // might as well calculated now
        GlobalCalc globalCalc = settings.getGlobalCalc(); 

        ClassStreamAttValVecI trainGlobalData =
            globalCalc.applyGlobals(trainStreamData);
        ClassStreamAttValVecI testGlobalData =
            globalCalc.applyGlobals(testStreamData);
        // And we might as well extract the events. 

        Debug.dp(Debug.PROGRESS, "PROGRESS: Globals calculated.");  
        Debug.dp(Debug.PROGRESS, "Train: " + trainGlobalData.size() +
                 " Test: " + testGlobalData.size());  


        ClassStreamEventsVecI trainEventData =
            evExtractor.extractEvents(trainStreamData); 
        ClassStreamEventsVecI testEventData =
            evExtractor.extractEvents(testStreamData); 

        Debug.dp(Debug.PROGRESS, "PROGRESS: Events extracted");  
        // System.out.println(trainEventData.toString()); 


        // Now we want the clustering algorithms only to cluster
        // instances of each class. Make an array of clusterers, 
        // one per class. 
        int numTestStreams = testEventData.size(); 

        int numClasses = domDesc.getClassDescVec().size(); 
        EventDescVecI eventDescVec = evExtractor.getDescription(); 
        EventClusterer[] eventClusterers = new
            EventClusterer[numClasses]; 
        // And now, initialise. 
        for(int i=0; i < numClasses; i++){
            // The new way: 
            eventClusterers[i] = settings.getEventClusterer(); 
            // The old way: 
            // eventClusterers[i] = new EventClusterer(new
            //    StreamTokenizer(
            //                    new FileReader(thisExp.evClusterDesc)), 
            //                   domDesc,
            //                   eventDescVec); 

            
            // System.out.println(eventClusterers[i]); 
        }

        // Segment the data. 


        ClassStreamEventsVec[] trainStreamsByClass = new
            ClassStreamEventsVec[numClasses]; 
        for(int i=0; i < numClasses; i++){
            trainStreamsByClass[i] = new ClassStreamEventsVec(); 
            trainStreamsByClass[i].setClassVec(new
                ClassificationVec()); 
            trainStreamsByClass[i].setStreamEventsVec(new
                StreamEventsVec()); 
    
        }

        Debug.dp(Debug.PROGRESS, "PROGRESS: Data rearranged.");  


        //And now load it up. 
        StreamEventsVecI trainEventSEV =
            trainEventData.getStreamEventsVec(); 
        ClassificationVecI trainEventCV = trainEventData.getClassVec();
        int numTrainStreams = trainEventCV.size(); 
        for(int i=0; i < numTrainStreams; i++){
            int currentClass = trainEventCV.elAt(i).getRealClass(); 
            trainStreamsByClass[currentClass].add(trainEventSEV.elAt(i), trainEventCV.elAt(i)); 
        }

        ClusterVecI[] clustersByClass = new ClusterVecI[numClasses]; 
        for(int i=0; i < numClasses; i++){
            clustersByClass[i] =
                eventClusterers[i].clusterEvents(trainStreamsByClass[i]); 
            Debug.dp(Debug.PROGRESS, "PROGRESS: Clustering of " + i + " complete"); 
            Debug.dp(Debug.PROGRESS, "Clusters for class: " + domDesc.getClassDescVec().getClassLabel(i) + " are:"); 
            Debug.dp(Debug.PROGRESS, eventClusterers[i].getMapping()); 
    
        }

        Debug.dp(Debug.PROGRESS, "PROGRESS: Clustering complete. ");  



        // But wait! There's more! There is always more. 
        // The first thing was only useful for clustering. 
        // Now attribution. We want to attribute all the data. So we are going 
        // to have one dataset for each learner. 
        // First set up the attributors. 

        Attributor[] attribsByClass = new Attributor[numClasses]; 
        for(int i=0; i < numClasses; i++){
            attribsByClass[i] = new Attributor(domDesc,
                                               clustersByClass[i], 
                                               eventClusterers[i].getDescription()); 

            Debug.dp(Debug.PROGRESS, "PROGRESS: AttributorMkr of " + i + " complete."); 
        }


        ClassStreamAttValVecI[] trainEventAtts = new
            ClassStreamAttValVec[numClasses]; 
        ClassStreamAttValVecI[] testEventAtts = new
            ClassStreamAttValVec[numClasses]; 

        for(int i=0; i < numClasses; i++){
            trainEventAtts[i] =
                attribsByClass[i].attribute(trainStreamData,
                                            trainEventData); 
            testEventAtts[i] = attribsByClass[i].attribute(testStreamData,
                                                           testEventData); 
            Debug.dp(Debug.PROGRESS, "PROGRESS: Attribution of " + i + " complete."); 

        }


        Debug.dp(Debug.PROGRESS, "PROGRESS: Attribution complete.");  


        // Combine all data sources. For now, globals go in every
        // one. 

        Combiner c = new Combiner(); 

        ClassStreamAttValVecI[] trainAttsByClass = new
            ClassStreamAttValVec[numClasses];

        ClassStreamAttValVecI[] testAttsByClass = new
            ClassStreamAttValVec[numClasses];

        for(int i=0; i < numClasses; i++){
            trainAttsByClass[i] = c.combine(trainGlobalData,
                                            trainEventAtts[i]); 

            testAttsByClass[i] = c.combine(testGlobalData,
                                           testEventAtts[i]); 
        }


        // Now we have to do some garbage collection. 

        trainStreamData = null; 
        testStreamData = null; 
        eventClusterers = null; 
        trainEventSEV = null; 
        trainEventCV = null; 
        clustersByClass = null; 
        attribsByClass = null; 

        System.gc(); 


        // So now we have the raw data in the correct form for each
        // attributor. 
        // And now, we can construct a learner for each case. 
        // Well, for now, I'm going to do something completely crazy. 
        // Let's run each classifier nonetheless over the whole data
        // ... and see what the hell happens. Maybe some voting scheme 
        // is possible!! This is a strange form of ensemble
        // classifier. 
        // Each naive bayes algorithm only gets one 

        Debug.setDebugLevel(Debug.PROGRESS); 

        AdaBoostM1[] dtLearners = new AdaBoostM1[numClasses]; 
        for(int i=0; i < numClasses; i++){
            dtLearners[i] = new AdaBoostM1();
            dtLearners[i].setClassifier(new J48()); 
            Debug.dp(Debug.PROGRESS, "PROGRESS: Beginning format conversion for class " + i); 
            Instances  data = WekaBridge.makeInstances(trainAttsByClass[i], "Train "+i);
            Debug.dp(Debug.PROGRESS, "PROGRESS: Conversion complete. Starting learning"); 
            dtLearners[i].buildClassifier(data); 
            Debug.dp(Debug.PROGRESS, "Learnt tree: \n" + dtLearners[i].toString()); 
        }

        ABClassifier[] dtClassifiers = new ABClassifier[numClasses]; 
        for(int i=0; i < numClasses; i++){
            dtClassifiers[i] =
                new ABClassifier(dtLearners[i]); 
            // System.out.println(nbClassifiers[i].toString()); 
        }

        Debug.dp(Debug.PROGRESS, "PROGRESS: Learning complete. ");  

        // Now test on training data (each one)
        /*
          for(int i=0; i < numClasses; i++){
          String className =
          domDesc.getClassDescVec().getClassLabel(i); 
          ClassificationVecI classvi = (ClassificationVecI) trainAttsByClass[i].getClassVec().clone();
          StreamAttValVecI savvi =
          trainAttsByClass[i].getStreamAttValVec(); 
    
          for(int j=0; j < trainAttsByClass[i].size(); j++){
          nbClassifiers[i].classify(savvi.elAt(j), classvi.elAt(j));
          }
          System.out.println(">>> Learner for class " + className); 
          int numCorrect = 0; 
          for(int j=0; j < classvi.size(); j++){
          System.out.print(classvi.elAt(j).toString()); 
          if(classvi.elAt(j).getRealClass() == classvi.elAt(j).getPredictedClass()){
          numCorrect++; 
          }
    
          }
          System.out.println("Train accuracy for " + className + " classifier: " + numCorrect + " of " + numTrainStreams + " (" + 
          numCorrect*100.0/numTrainStreams + "%)"); 

    
          }
        */

        System.out.println(">>> Testing stage <<<"); 
        // First, print the results of using the straight testers. 
        ClassificationVecI[] classns = new ClassificationVecI[numClasses]; 
        for(int i=0; i < numClasses; i++){
            String className =
                domDesc.getClassDescVec().getClassLabel(i); 
            classns[i] = (ClassificationVecI) testAttsByClass[i].getClassVec().clone();
            StreamAttValVecI savvi =
                testAttsByClass[i].getStreamAttValVec(); 
            Instances data = WekaBridge.makeInstances(testAttsByClass[i], "Test " + i); 
            for(int j=0; j < numTestStreams; j++){
                dtClassifiers[i].classify(data.instance(j), classns[i].elAt(j));
            }
            System.out.println(">>> Learner for class " + className); 
            int numCorrect = 0; 
            for(int j=0; j < numTestStreams; j++){
                System.out.print(classns[i].elAt(j).toString()); 
                if(classns[i].elAt(j).getRealClass() == classns[i].elAt(j).getPredictedClass()){
                    numCorrect++; 
                }
    
            }
            System.out.println("Test accuracy for " + className + " classifier: " + numCorrect + " of " + numTestStreams + " (" + 
                               numCorrect*100.0/numTestStreams + "%)"); 

    
        }

        // Now do voting. This is a hack solution. 
        int numCorrect = 0; 
        for(int i=0; i < numTestStreams; i++){
            int[] votes = new int[numClasses]; 
            int realClass = classns[0].elAt(i).getRealClass();
            String realClassName = domDesc.getClassDescVec().getClassLabel(realClass); 
            for(int j=0; j < numClasses; j++){
                int thisPrediction = classns[j].elAt(i).getPredictedClass(); 

                // if(thisPrediction == j){
                //     votes[thisPrediction] += 2; 
                // }
                //else {
                votes[thisPrediction]++; 
                //}

            }
            int maxIndex = -1;
            int maxVotes = 0; 
            String voteRes = "[ "; 
            for(int j=0; j <numClasses; j++){
                voteRes += votes[j] + " "; 
                if(votes[j] > maxVotes){
                    maxIndex = j;
                    maxVotes = votes[j]; 
                }
            } 
            voteRes += "]"; 
            // Now print the result: 
            String predictedClassName = domDesc.getClassDescVec().getClassLabel(maxIndex); 
            if(maxIndex == realClass){ 
                System.out.println("Class " + realClassName + " CORRECTLY classified with " + maxVotes + " votes. Votes: " + voteRes); 
                numCorrect++; 
            }
            else {
                System.out.println("Class " + realClassName + " INCORRECTLY classified as " + predictedClassName + " with " + maxVotes  + " votes. Votes: " + voteRes); 
            }
    
        }
        System.out.println("Final voted accuracy: " + numCorrect + " of " + numTestStreams + " (" + 
                           numCorrect*100.0/numTestStreams + "%)"); 
    }
    
}