Java tutorial
/* * This file is part of JaTeCS. * * JaTeCS 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. * * JaTeCS 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 JaTeCS. If not, see <http://www.gnu.org/licenses/>. * * The software has been mainly developed by (in alphabetical order): * - Andrea Esuli (andrea.esuli@isti.cnr.it) * - Tiziano Fagni (tiziano.fagni@isti.cnr.it) * - Alejandro Moreo Fernndez (alejandro.moreo@isti.cnr.it) * Other past contributors were: * - Giacomo Berardi (giacomo.berardi@isti.cnr.it) */ package apps.quantification; import gnu.trove.TShortDoubleHashMap; import it.cnr.jatecs.classification.ClassificationMode; import it.cnr.jatecs.classification.interfaces.IDataManager; import it.cnr.jatecs.classification.svmlight.SvmPerfClassifierCustomizer; import it.cnr.jatecs.classification.svmlight.SvmPerfDataManager; import it.cnr.jatecs.classification.svmlight.SvmPerfLearner; import it.cnr.jatecs.classification.svmlight.SvmPerfLearnerCustomizer; import it.cnr.jatecs.evaluation.ContingencyTable; import it.cnr.jatecs.evaluation.ContingencyTableSet; import it.cnr.jatecs.indexes.DB.interfaces.IIndex; import it.cnr.jatecs.indexes.DB.troveCompact.TroveClassificationDBType; import it.cnr.jatecs.indexes.DB.troveCompact.TroveContentDBType; import it.cnr.jatecs.indexes.DB.troveCompact.TroveReadWriteHelper; import it.cnr.jatecs.io.FileSystemStorageManager; import it.cnr.jatecs.quantification.QuantificationLearner; import it.cnr.jatecs.quantification.interfaces.IQuantifier; import it.cnr.jatecs.quantification.scalingFunctions.LogisticFunction; import it.cnr.jatecs.utils.IOperationStatusListener; import it.cnr.jatecs.utils.Os; import it.cnr.jatecs.utils.TextualProgressBar; import org.apache.commons.cli.*; import java.io.BufferedWriter; import java.io.File; import java.io.FileWriter; import java.io.IOException; /** * This app learns a pool of quantification models out of a training set using * SVM_perf as the base learner. * <p> * The SVM_perf learn executable must be locally available. * * @author Andrea Esuli */ public class LearnQuantificationSVMPerf { public static void main(String[] args) throws IOException { String cmdLineSyntax = LearnQuantificationSVMPerf.class.getName() + " [OPTIONS] <path to svm_perf_learn> <path to svm_perf_classify> <trainingIndexDirectory> <outputDirectory>"; Options options = new Options(); OptionBuilder.withArgName("f"); OptionBuilder.withDescription("Number of folds"); OptionBuilder.withLongOpt("f"); OptionBuilder.isRequired(true); OptionBuilder.hasArg(); options.addOption(OptionBuilder.create()); OptionBuilder.withArgName("c"); OptionBuilder.withDescription("The c value for svm_perf (default 0.01)"); OptionBuilder.withLongOpt("c"); OptionBuilder.isRequired(false); OptionBuilder.hasArg(); options.addOption(OptionBuilder.create()); OptionBuilder.withArgName("t"); OptionBuilder.withDescription("Path for temporary files"); OptionBuilder.withLongOpt("t"); OptionBuilder.isRequired(false); OptionBuilder.hasArg(); options.addOption(OptionBuilder.create()); OptionBuilder.withArgName("l"); OptionBuilder.withDescription("The loss function to optimize (default 2):\n" + " 0 Zero/one loss: 1 if vector of predictions contains error, 0 otherwise.\n" + " 1 F1: 100 minus the F1-score in percent.\n" + " 2 Errorrate: Percentage of errors in prediction vector.\n" + " 3 Prec/Rec Breakeven: 100 minus PRBEP in percent.\n" + " 4 Prec@p: 100 minus precision at p in percent.\n" + " 5 Rec@p: 100 minus recall at p in percent.\n" + " 10 ROCArea: Percentage of swapped pos/neg pairs (i.e. 100 - ROCArea)."); OptionBuilder.withLongOpt("l"); OptionBuilder.isRequired(false); OptionBuilder.hasArg(); options.addOption(OptionBuilder.create()); OptionBuilder.withArgName("w"); OptionBuilder.withDescription("Choice of structural learning algorithm (default 9):\n" + " 0: n-slack algorithm described in [2]\n" + " 1: n-slack algorithm with shrinking heuristic\n" + " 2: 1-slack algorithm (primal) described in [5]\n" + " 3: 1-slack algorithm (dual) described in [5]\n" + " 4: 1-slack algorithm (dual) with constraint cache [5]\n" + " 9: custom algorithm in svm_struct_learn_custom.c"); OptionBuilder.withLongOpt("w"); OptionBuilder.isRequired(false); OptionBuilder.hasArg(); options.addOption(OptionBuilder.create()); OptionBuilder.withArgName("p"); OptionBuilder.withDescription("The value of p used by the prec@p and rec@p loss functions (default 0)"); OptionBuilder.withLongOpt("p"); OptionBuilder.isRequired(false); OptionBuilder.hasArg(); options.addOption(OptionBuilder.create()); OptionBuilder.withArgName("v"); OptionBuilder.withDescription("Verbose output"); OptionBuilder.withLongOpt("v"); OptionBuilder.isRequired(false); OptionBuilder.hasArg(false); options.addOption(OptionBuilder.create()); OptionBuilder.withArgName("s"); OptionBuilder.withDescription("Don't delete temporary training file in svm_perf format (default: delete)"); OptionBuilder.withLongOpt("s"); OptionBuilder.isRequired(false); OptionBuilder.hasArg(false); options.addOption(OptionBuilder.create()); SvmPerfLearnerCustomizer classificationLearnerCustomizer = null; SvmPerfClassifierCustomizer classificationCustomizer = null; int folds = -1; GnuParser parser = new GnuParser(); String[] remainingArgs = null; try { CommandLine line = parser.parse(options, args); remainingArgs = line.getArgs(); classificationLearnerCustomizer = new SvmPerfLearnerCustomizer(remainingArgs[0]); classificationCustomizer = new SvmPerfClassifierCustomizer(remainingArgs[1]); folds = Integer.parseInt(line.getOptionValue("f")); if (line.hasOption("c")) classificationLearnerCustomizer.setC(Float.parseFloat(line.getOptionValue("c"))); if (line.hasOption("w")) classificationLearnerCustomizer.setW(Integer.parseInt(line.getOptionValue("w"))); if (line.hasOption("p")) classificationLearnerCustomizer.setP(Integer.parseInt(line.getOptionValue("p"))); if (line.hasOption("l")) classificationLearnerCustomizer.setL(Integer.parseInt(line.getOptionValue("l"))); if (line.hasOption("v")) classificationLearnerCustomizer.printSvmPerfOutput(true); if (line.hasOption("s")) classificationLearnerCustomizer.setDeleteTrainingFiles(false); if (line.hasOption("t")) { classificationLearnerCustomizer.setTempPath(line.getOptionValue("t")); classificationCustomizer.setTempPath(line.getOptionValue("t")); } } catch (Exception exp) { System.err.println("Parsing failed. Reason: " + exp.getMessage()); HelpFormatter formatter = new HelpFormatter(); formatter.printHelp(cmdLineSyntax, options); System.exit(-1); } assert (classificationLearnerCustomizer != null); if (remainingArgs.length != 4) { HelpFormatter formatter = new HelpFormatter(); formatter.printHelp(cmdLineSyntax, options); System.exit(-1); } String indexFile = remainingArgs[2]; File file = new File(indexFile); String indexName = file.getName(); String indexPath = file.getParent(); String outputPath = remainingArgs[3]; SvmPerfLearner classificationLearner = new SvmPerfLearner(); classificationLearner.setRuntimeCustomizer(classificationLearnerCustomizer); FileSystemStorageManager fssm = new FileSystemStorageManager(indexPath, false); fssm.open(); IIndex training = TroveReadWriteHelper.readIndex(fssm, indexName, TroveContentDBType.Full, TroveClassificationDBType.Full); final TextualProgressBar progressBar = new TextualProgressBar("Learning the quantifiers"); IOperationStatusListener status = new IOperationStatusListener() { @Override public void operationStatus(double percentage) { progressBar.signal((int) percentage); } }; QuantificationLearner quantificationLearner = new QuantificationLearner(folds, classificationLearner, classificationLearnerCustomizer, classificationCustomizer, ClassificationMode.PER_CATEGORY, new LogisticFunction(), status); IQuantifier[] quantifiers = quantificationLearner.learn(training); File executableFile = new File(classificationLearnerCustomizer.getSvmPerfLearnPath()); IDataManager classifierDataManager = new SvmPerfDataManager(new SvmPerfClassifierCustomizer( executableFile.getParentFile().getAbsolutePath() + Os.pathSeparator() + "svm_perf_classify")); String description = "_SVMPerf_C-" + classificationLearnerCustomizer.getC() + "_W-" + classificationLearnerCustomizer.getW() + "_L-" + classificationLearnerCustomizer.getL(); if (classificationLearnerCustomizer.getL() == 4 || classificationLearnerCustomizer.getL() == 5) description += "_P-" + classificationLearnerCustomizer.getP(); if (classificationLearnerCustomizer.getAdditionalParameters().length() > 0) description += "_" + classificationLearnerCustomizer.getAdditionalParameters(); String quantifierPrefix = indexName + "_Quantifier-" + folds + description; FileSystemStorageManager fssmo = new FileSystemStorageManager( outputPath + File.separatorChar + quantifierPrefix, true); fssmo.open(); QuantificationLearner.write(quantifiers, fssmo, classifierDataManager); fssmo.close(); BufferedWriter bfs = new BufferedWriter( new FileWriter(outputPath + File.separatorChar + quantifierPrefix + "_rates.txt")); TShortDoubleHashMap simpleTPRs = quantificationLearner.getSimpleTPRs(); TShortDoubleHashMap simpleFPRs = quantificationLearner.getSimpleFPRs(); TShortDoubleHashMap scaledTPRs = quantificationLearner.getScaledTPRs(); TShortDoubleHashMap scaledFPRs = quantificationLearner.getScaledFPRs(); ContingencyTableSet contingencyTableSet = quantificationLearner.getContingencyTableSet(); short[] cats = simpleTPRs.keys(); for (int i = 0; i < cats.length; ++i) { short cat = cats[i]; String catName = training.getCategoryDB().getCategoryName(cat); ContingencyTable contingencyTable = contingencyTableSet.getCategoryContingencyTable(cat); double simpleTPR = simpleTPRs.get(cat); double simpleFPR = simpleFPRs.get(cat); double scaledTPR = scaledTPRs.get(cat); double scaledFPR = scaledFPRs.get(cat); String line = quantifierPrefix + "\ttrain\tsimple\t" + catName + "\t" + cat + "\t" + contingencyTable.tp() + "\t" + contingencyTable.fp() + "\t" + contingencyTable.fn() + "\t" + contingencyTable.tn() + "\t" + simpleTPR + "\t" + simpleFPR + "\n"; bfs.write(line); line = quantifierPrefix + "\ttrain\tscaled\t" + catName + "\t" + cat + "\t" + contingencyTable.tp() + "\t" + contingencyTable.fp() + "\t" + contingencyTable.fn() + "\t" + contingencyTable.tn() + "\t" + scaledTPR + "\t" + scaledFPR + "\n"; bfs.write(line); } bfs.close(); } }