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. That is to say, we cluster all the instances * using the same clustering algorithms. * * * @author Waleed Kadous * @version $Id: ExpSingle.java,v 1.1.1.1 2002/06/28 07:36:16 waleed Exp $ */ package tclass; import java.util.StringTokenizer; import tclass.clusteralg.GClust; import tclass.util.Debug; import weka.attributeSelection.BestFirst; import weka.attributeSelection.CfsSubsetEval; import weka.classifiers.AbstractClassifier; import weka.classifiers.Classifier; import weka.core.Instances; import weka.core.Utils; import weka.filters.Filter; import weka.filters.unsupervised.attribute.Remove; public class ExpSingle { // Ok. What we are going to do is to separate the learning task in // an interesting way. // First of all, though, the standard stuff static Runtime rt = Runtime.getRuntime(); 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 learnerStuff = weka.classifiers.trees.J48.class.getName(); boolean featureSel = false; boolean makeDesc = false; boolean trainResults = false; void parseArgs(String[] args) { for (int i = 0; i < args.length; i++) { if (args[i].equals("-tr")) { trainDataFile = args[++i]; } if (args[i].equals("-dd")) { domDescFile = 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("-md")) { makeDesc = true; } if (args[i].equals("-trainres")) { trainResults = true; } if (args[i].equals("-l")) { learnerStuff = args[++i]; learnerStuff = learnerStuff.replace(':', ' '); System.err.println("Learner String is: " + learnerStuff); } } } // Alright. This is downright funky hacky stuff. public static void mem(String label) { System.out.println("Memory at checkpt " + label + ": " + (rt.totalMemory() / 1024 / 1024) + " megabytes"); } public static void main(String[] args) throws Exception { Debug.setDebugLevel(Debug.PROGRESS); ExpSingle thisExp = new ExpSingle(); thisExp.parseArgs(args); mem("PARSE"); 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"); mem("DATAIN"); 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."); mem("GLOBAL"); 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"); mem("EVENTEXTRACT"); // 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."); mem("REARRANGE"); //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, "\n" + eventClusterer.getMapping()); Debug.dp(Debug.PROGRESS, "PROGRESS: Clustering complete. "); mem("CLUSTER"); // 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."); mem("MAKEATTRIBUTOR"); ClassStreamAttValVecI trainEventAtts = attribs.attribute(trainStreamData, trainEventData); ClassStreamAttValVecI testEventAtts = attribs.attribute(testStreamData, testEventData); Debug.dp(Debug.PROGRESS, "PROGRESS: Attribution complete."); mem("ATTRIBUTION"); // 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); mem("COMBINATION"); trainStreamData = null; testStreamData = null; trainEventSEV = null; trainEventCV = null; if (!thisExp.makeDesc) { clusters = null; eventClusterer = null; } attribs = null; System.gc(); mem("GARBAGECOLLECT"); // 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); int[] selectedIndices = null; String[] classifierSpec = Utils.splitOptions(thisExp.learnerStuff); if (classifierSpec.length == 0) { throw new Exception("Invalid classifier specification string"); } String classifierName = classifierSpec[0]; classifierSpec[0] = ""; Classifier learner = AbstractClassifier.forName(classifierName, classifierSpec); 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"); mem("ATTCONVERSION"); 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] + 1) + ","; } featureString += ("last"); System.err.println(featureString); // Now apply the filter. Remove af = new Remove(); af.setInvertSelection(true); af.setAttributeIndices(featureString); af.setInputFormat(data); data = Filter.useFilter(data, af); } learner.buildClassifier(data); mem("POSTLEARNER"); Debug.dp(Debug.PROGRESS, "Learnt classifier: \n" + learner.toString()); WekaClassifier wekaClassifier; wekaClassifier = new WekaClassifier(learner); if (thisExp.makeDesc) { // Section for making description more readable. Assumes that // learner.toString() returns a string with things that look like // feature names. String concept = learner.toString(); StringTokenizer st = new StringTokenizer(concept, " \t\r\n", true); int evId = 1; String evIndex = ""; while (st.hasMoreTokens()) { boolean appendColon = false; String curTok = st.nextToken(); GClust clust = (GClust) ((ClusterVec) clusters).elCalled(curTok); if (clust != null) { // Skip the spaces st.nextToken(); // Get a < or > String cmp = st.nextToken(); String qual = ""; if (cmp.equals("<=")) { qual = " HAS NO "; } else { qual = " HAS "; } // skip spaces st.nextToken(); // Get the number. String conf = st.nextToken(); if (conf.endsWith(":")) { conf = conf.substring(0, conf.length() - 1); appendColon = true; } float minconf = Float.valueOf(conf).floatValue(); EventI[] res = clust.getBounds(minconf); String name = clust.getName(); int dashPos = name.indexOf('-'); int undPos = name.indexOf('_'); String chan = name.substring(0, dashPos); String evType = name.substring(dashPos + 1, undPos); EventDescI edi = clust.eventDesc(); if (qual == " HAS NO " && thisExp.learnerStuff.startsWith(weka.classifiers.trees.J48.class.getName())) { System.out.print("OTHERWISE"); } else { System.out.print("IF " + chan + qual + res[2] + " (*" + evId + ")"); int numParams = edi.numParams(); evIndex += "*" + evId + ": " + evType + "\n"; for (int i = 0; i < numParams; i++) { evIndex += " " + edi.paramName(i) + "=" + res[2].valOf(i) + " r=[" + res[0].valOf(i) + "," + res[1].valOf(i) + "]\n"; } evId++; } evIndex += "\n"; if (appendColon) { System.out.print(" THEN"); } } else { System.out.print(curTok); } } System.out.println("\nEvent index"); System.out.println("-----------"); System.out.print(evIndex); mem("POSTDESC"); // Now this is going to be messy as fuck. Really. What do we needs? Well, // we need to read in the data; look up some info, that we // assume came from a GainClusterer ... // Sanity check. // GClust clust = (GClust) ((ClusterVec) clusters).elCalled("alpha-inc_0"); // System.out.println("INSANE!: " + clust.getDescription()); // EventI[] res = clust.getBounds(1); // System.out.println("For clust settings: min event = " + res[0].toString() + " and max event = " + res[1].toString()); } Debug.dp(Debug.PROGRESS, "PROGRESS: Learning complete. "); int numCorrect = 0; ClassificationVecI classns; if (thisExp.trainResults) { System.err.println(">>> Training performance <<<"); classns = (ClassificationVecI) trainAtts.getClassVec().clone(); for (int j = 0; j < numTrainStreams; j++) { wekaClassifier.classify(data.instance(j), classns.elAt(j)); } for (int j = 0; j < numTrainStreams; 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.err.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.err.println( "Class " + realClassName + " INCORRECTLY classified as " + predictedClassName + "."); } } System.err.println("Training results for classifier: " + numCorrect + " of " + numTrainStreams + " (" + numCorrect * 100.0 / numTrainStreams + "%)"); } mem("POSTTRAIN"); System.err.println(">>> Testing stage <<<"); // First, print the results of using the straight testers. classns = (ClassificationVecI) testAtts.getClassVec().clone(); StreamAttValVecI savvi = testAtts.getStreamAttValVec(); data = WekaBridge.makeInstances(testAtts, "Test "); 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. Remove af = new Remove(); af.setInvertSelection(true); af.setAttributeIndices(featureString); af.setInputFormat(data); data = Filter.useFilter(data, af); } for (int j = 0; j < numTestStreams; j++) { wekaClassifier.classify(data.instance(j), classns.elAt(j)); } System.err.println(">>> Learner <<<"); 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.err.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.err.println( "Class " + realClassName + " INCORRECTLY classified as " + predictedClassName + "."); } } System.err.println("Test accuracy for classifier: " + numCorrect + " of " + numTestStreams + " (" + numCorrect * 100.0 / numTestStreams + "%)"); mem("POSTTEST"); } }