Java tutorial
/* * 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 3 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, see <http://www.gnu.org/licenses/>. */ /** * Single classifier solution. * * Superseded by ExpSingle * * @author Waleed Kadous * @version $Id: ExpNB_Single.java,v 1.1.1.1 2002/06/28 07:36:16 waleed Exp $ */ package tclass; import tclass.learnalg.NaiveBayes; import tclass.util.Debug; import weka.attributeSelection.BestFirst; import weka.attributeSelection.CfsSubsetEval; import weka.core.Instances; public class ExpNB_Single { // 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"; boolean featureSel = false; int numDivs = 10; 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("-settings")) { settingsFile = args[++i]; } if (args[i].equals("-fs")) { featureSel = true; } if (args[i].equals("-numdivs")) { numDivs = Integer.parseInt(args[++i]); } } } public static void main(String[] args) throws Exception { Debug.setDebugLevel(Debug.PROGRESS); ExpSingle thisExp = new ExpSingle(); 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 eventClusterer = settings.getEventClusterer(); Debug.dp(Debug.PROGRESS, "PROGRESS: Data rearranged."); //And now load it up. StreamEventsVecI trainEventSEV = trainEventData.getStreamEventsVec(); ClassificationVecI trainEventCV = trainEventData.getClassVec(); int numTrainStreams = trainEventCV.size(); ClusterVecI clusters = eventClusterer.clusterEvents(trainEventData); Debug.dp(Debug.PROGRESS, "PROGRESS: Clustering complete"); Debug.dp(Debug.PROGRESS, "Clusters are:"); Debug.dp(Debug.PROGRESS, eventClusterer.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 attribs = new Attributor(domDesc, clusters, eventClusterer.getDescription()); Debug.dp(Debug.PROGRESS, "PROGRESS: AttributorMkr complete."); ClassStreamAttValVecI trainEventAtts = attribs.attribute(trainStreamData, trainEventData); ClassStreamAttValVecI testEventAtts = attribs.attribute(testStreamData, testEventData); Debug.dp(Debug.PROGRESS, "PROGRESS: Attribution complete."); // Combine all data sources. For now, globals go in every // one. Combiner c = new Combiner(); ClassStreamAttValVecI trainAtts = c.combine(trainGlobalData, trainEventAtts); ClassStreamAttValVecI testAtts = c.combine(testGlobalData, testEventAtts); trainStreamData = null; testStreamData = null; eventClusterer = null; trainEventSEV = null; trainEventCV = null; clusters = null; attribs = 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.dp(Debug.PROGRESS, "PROGRESS: Beginning format conversion for class "); Instances data = WekaBridge.makeInstances(trainAtts, "Train "); Debug.dp(Debug.PROGRESS, "PROGRESS: Conversion complete. Starting learning"); Debug.setDebugLevel(Debug.PROGRESS); int[] selectedIndices = null; if (thisExp.featureSel) { Debug.dp(Debug.PROGRESS, "PROGRESS: Doing feature selection"); BestFirst bfs = new BestFirst(); CfsSubsetEval cfs = new CfsSubsetEval(); cfs.buildEvaluator(data); selectedIndices = bfs.search(cfs, data); // Now extract the features. System.err.print("Selected features: "); String featureString = new String(); for (int j = 0; j < selectedIndices.length; j++) { featureString += selectedIndices[j] + ","; } featureString += ("last"); System.err.println(featureString); // Now cut from trainAtts. // trainAtts.selectFeatures(selectedIndices); } Debug.dp(Debug.PROGRESS, "Learning with Naive Bayes now ..."); NaiveBayes nbLearner = new NaiveBayes(); nbLearner.setDomDesc(domDesc); nbLearner.setAttDescVec(trainAtts.getStreamAttValVec().getDescription()); ClassifierI nbClassifier = nbLearner.learn(trainAtts); Debug.dp(Debug.PROGRESS, "PROGRESS: Learning complete. "); System.out.println(">>> Testing stage <<<"); // First, print the results of using the straight testers. ClassificationVecI classns; classns = (ClassificationVecI) testAtts.getClassVec().clone(); StreamAttValVecI savvi = testAtts.getStreamAttValVec(); /* if(thisExp.featureSel){ String featureString = new String(); for(int j=0; j < selectedIndices.length; j++){ featureString += (selectedIndices[j]+1) + ","; } featureString += "last"; // Now apply the filter. AttributeFilter af = new AttributeFilter(); af.setInvertSelection(true); af.setAttributeIndices(featureString); af.inputFormat(data); data = af.useFilter(data, af); } */ for (int j = 0; j < numTestStreams; j++) { nbClassifier.classify(savvi.elAt(j), classns.elAt(j)); } System.out.println(">>> Learner <<<"); int numCorrect = 0; for (int j = 0; j < numTestStreams; j++) { System.out.print(classns.elAt(j).toString()); if (classns.elAt(j).getRealClass() == classns.elAt(j).getPredictedClass()) { numCorrect++; String realClassName = domDesc.getClassDescVec().getClassLabel(classns.elAt(j).getRealClass()); System.out.println("Class " + realClassName + " CORRECTLY classified."); } else { String realClassName = domDesc.getClassDescVec().getClassLabel(classns.elAt(j).getRealClass()); String predictedClassName = domDesc.getClassDescVec() .getClassLabel(classns.elAt(j).getPredictedClass()); System.out.println( "Class " + realClassName + " INCORRECTLY classified as " + predictedClassName + "."); } } System.out.println("Test accuracy for classifier: " + numCorrect + " of " + numTestStreams + " (" + numCorrect * 100.0 / numTestStreams + "%)"); } }