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/>. */ /** * ToArff.java: Converts a dataset into a format palatable to * Weka's learners. Makes it easier to explore the data. * * * @author Waleed Kadous * @version $Id: ToArff.java,v 1.1 2002/08/02 05:07:52 waleed Exp $ * $Log: ToArff.java,v $ * Revision 1.1 2002/08/02 05:07:52 waleed * *** empty log message *** * */ package tclass; import java.io.FileWriter; 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 ToArff { // 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 inFile = "sl.tsl"; // String globalDesc = "test._gc"; // String evExtractDesc = "test._ee"; String settingsFile = "test.tal"; String learnerStuff = weka.classifiers.trees.J48.class.getName(); String outFile = "default.arff"; 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("-in")) { inFile = args[++i]; } if (args[i].equals("-out")) { outFile = args[++i]; } if (args[i].equals("-dd")) { domDescFile = 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 main(String[] args) throws Exception { Debug.setDebugLevel(Debug.PROGRESS); ToArff thisExp = new ToArff(); thisExp.parseArgs(args); DomDesc domDesc = new DomDesc(thisExp.domDescFile); ClassStreamVecI trainStreamData = new ClassStreamVec(thisExp.inFile, 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); // And we might as well extract the events. Debug.dp(Debug.PROGRESS, "PROGRESS: Globals calculated."); Debug.dp(Debug.PROGRESS, "Train: " + trainGlobalData.size()); ClassStreamEventsVecI trainEventData = evExtractor.extractEvents(trainStreamData); 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 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, "\n" + 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); 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); trainStreamData = null; trainEventSEV = null; trainEventCV = null; if (!thisExp.makeDesc) { clusters = null; eventClusterer = 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.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"); 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); } try { FileWriter fw = new FileWriter(thisExp.outFile); fw.write(data.toString()); fw.close(); } catch (Exception e) { throw new Exception("Could not write to output file. "); } } }