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From source file:akori.Impact.java
static public void main(String[] args) throws IOException { String PATH = "E:\\Trabajos\\AKORI\\datosmatrizgino\\"; String PATHIMG = "E:\\NetBeansProjects\\AKORI\\Proccess_1\\ImagesPages\\"; for (int i = 1; i <= 32; ++i) { for (int k = 1; k <= 15; ++k) { System.out.println("Matrix " + i + "-" + k); BufferedImage img = null; try { img = ImageIO.read(new File(PATHIMG + i + ".png")); } catch (IOException ex) { ex.getStackTrace();/* w w w.j a v a2 s . c om*/ } int ymax = img.getHeight(); int xmax = img.getWidth(); double[][] imagen = new double[ymax][xmax]; BufferedReader in = null; try { in = new BufferedReader(new FileReader(PATH + i + "-" + k + ".txt")); } catch (FileNotFoundException ex) { ex.getStackTrace(); } String linea; ArrayList<String> lista = new ArrayList<String>(); HashMap<String, String> lista1 = new HashMap<String, String>(); try { for (int j = 0; (linea = in.readLine()) != null; ++j) { String[] datos = linea.split(","); int x = (int) Double.parseDouble(datos[1]); int y = (int) Double.parseDouble(datos[2]); if (x >= xmax || y >= ymax || x <= 0 || y <= 0) { continue; } lista.add(x + "," + y); } } catch (Exception ex) { ex.getStackTrace(); } try { in.close(); } catch (IOException ex) { ex.getStackTrace(); } Iterator iter = lista.iterator(); int[][] matrix = new int[lista.size()][2]; for (int j = 0; iter.hasNext(); ++j) { String xy = (String) iter.next(); String[] datos = xy.split(","); matrix[j][0] = Integer.parseInt(datos[0]); matrix[j][1] = Integer.parseInt(datos[1]); } for (int j = 0; j < matrix.length; ++j) { int std = 50; int x = matrix[j][0]; int y = matrix[j][1]; imagen[y][x] += 1; double aux; normalMatrix(imagen, y, x, std); } FileWriter fw = new FileWriter(PATH + "Matrix" + i + "-" + k + ".txt"); BufferedWriter bw = new BufferedWriter(fw); for (int j = 0; j < imagen.length; ++j) { for (int t = 0; t < imagen[j].length; ++t) { if (t + 1 == imagen[j].length) bw.write(imagen[j][t] + ""); else bw.write(imagen[j][t] + ","); } bw.write("\n"); } bw.close(); } } }
From source file:Anaphora_Resolution.ParseAllXMLDocuments.java
public static void main(String[] args) throws IOException, SAXException, ParserConfigurationException, TransformerException { // File dataFolder = new File("DataToPort"); // File[] documents; String grammar = "grammar/englishPCFG.ser.gz"; String[] options = { "-maxLength", "100", "-retainTmpSubcategories" }; //LexicalizedParser lp = new LexicalizedParser(grammar, options); LexicalizedParser lp = LexicalizedParser.loadModel("edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"); ////from w w w . jav a 2s . c o m // if (dataFolder.isDirectory()) { // documents = dataFolder.listFiles(); // } else { // documents = new File[] {dataFolder}; // } // int currfile = 0; // int totfiles = documents.length; // for (File paper : documents) { // currfile++; // if (paper.getName().equals(".DS_Store")||paper.getName().equals(".xml")) { // currfile--; // totfiles--; // continue; // } // System.out.println("Working on "+paper.getName()+" (file "+currfile+" out of "+totfiles+")."); // // DocumentBuilderFactory docFactory = DocumentBuilderFactory.newInstance(); // This is for XML // DocumentBuilder docBuilder = docFactory.newDocumentBuilder(); // Document doc = docBuilder.parse(paper.getAbsolutePath()); // // NodeList textlist = doc.getElementsByTagName("text"); // for(int i=0; i < textlist.getLength(); i++) { // Node currentnode = textlist.item(i); // String wholetext = textlist.item(i).getTextContent(); String wholetext = "How about other changes for example the ways of doing the work and \n" + "\n" + "Iwould say security has , there 's more pressure put on people now than there used to be because obviously , especially after Locherbie , they tightened up on security and there 's a lot more pressure now especially from the ETR and stuff like that \n" + "People do n't feel valued any more , they feel I do n't know I think they feel that nobody cares about them really anyway \n"; //System.out.println(wholetext); //Iterable<List<? extends HasWord>> sentences; ArrayList<Tree> parseTrees = new ArrayList<Tree>(); String asd = ""; int j = 0; StringReader stringreader = new StringReader(wholetext); DocumentPreprocessor dp = new DocumentPreprocessor(stringreader); @SuppressWarnings("rawtypes") ArrayList<List> sentences = preprocess(dp); for (List sentence : sentences) { parseTrees.add(lp.apply(sentence)); // Parsing a new sentence and adding it to the parsed tree ArrayList<Tree> PronounsList = findPronouns(parseTrees.get(j)); // Locating all pronouns to resolve in the sentence Tree corefedTree; for (Tree pronounTree : PronounsList) { parseTrees.set(parseTrees.size() - 1, HobbsResolve(pronounTree, parseTrees)); // Resolving the coref and modifying the tree for each pronoun } StringWriter strwr = new StringWriter(); PrintWriter prwr = new PrintWriter(strwr); TreePrint tp = new TreePrint("penn"); tp.printTree(parseTrees.get(j), prwr); prwr.flush(); asd += strwr.toString(); j++; } String armando = ""; for (Tree sentence : parseTrees) { for (Tree leaf : Trees.leaves(sentence)) armando += leaf + " "; } System.out.println(wholetext); System.out.println(); System.out.println("......"); System.out.println(armando); System.out.println("All done."); // currentnode.setTextContent(asd); // } // TransformerFactory transformerFactory = TransformerFactory.newInstance(); // Transformer transformer = transformerFactory.newTransformer(); // DOMSource source = new DOMSource(doc); // StreamResult result = new StreamResult(paper); // transformer.transform(source, result); // // System.out.println("Done"); // } }
From source file:com.genentech.chemistry.tool.align.SDFAlign.java
public static void main(String... args) { Options options = new Options(); Option opt = new Option("in", true, "input sd file"); opt.setRequired(true);//from w w w. j ava 2s .c o m options.addOption(opt); opt = new Option("out", true, "output file"); opt.setRequired(true); options.addOption(opt); opt = new Option("method", true, "fss|sss|MCS|clique (default mcs)."); options.addOption(opt); opt = new Option("ref", true, "reference molecule if not given first in file is used. If multiple ref molecules are read the min RMSD is reported"); options.addOption(opt); opt = new Option("mirror", false, "If given and the molecule is not chiral, return best mirror image."); options.addOption(opt); opt = new Option("rmsdTag", true, "Tagname for output of rmsd, default: no output."); options.addOption(opt); opt = new Option("atomMatch", true, "Sequence of none|default|hcount|noAromatic specifing how atoms are matched cf. oe document.\n" + "noAromatic can be used to make terminal atoms match aliphatic and aromatic atoms.\n" + "Queryfeatures are considered only if default is used."); options.addOption(opt); opt = new Option("bondMatch", true, "Sequence of none|default specifing how bonds are matched cf. oe document."); options.addOption(opt); opt = new Option("keepCoreHydrogens", false, "If not specified the hydrigen atoms are removed from the core."); options.addOption(opt); opt = new Option("outputMol", true, "aligned|original (def: aligned) use original to just compute rmsd."); options.addOption(opt); opt = new Option("doNotOptimize", false, "If specified the RMSD is computed without moving optimizing the overlay."); options.addOption(opt); opt = new Option("quiet", false, "Reduced warining messages"); options.addOption(opt); CommandLineParser parser = new BasicParser(); CommandLine cmd = null; try { cmd = parser.parse(options, args); } catch (Exception e) { exitWithHelp(e.getMessage(), options); } if (cmd.getArgs().length > 0) exitWithHelp("To many arguments", options); // do not check aromaticity on atoms so that a terminal atom matches aromatic and non aromatic atoms int atomExpr = OEExprOpts.DefaultAtoms; int bondExpr = OEExprOpts.DefaultBonds; String atomMatch = cmd.getOptionValue("atomMatch"); if (atomMatch == null) atomMatch = ""; atomMatch = '|' + atomMatch.toLowerCase() + '|'; String bondMatch = cmd.getOptionValue("bondMatch"); if (bondMatch == null) bondMatch = ""; bondMatch = '|' + bondMatch.toLowerCase() + '|'; String inFile = cmd.getOptionValue("in"); String outFile = cmd.getOptionValue("out"); String refFile = cmd.getOptionValue("ref"); String method = cmd.getOptionValue("method"); String rmsdTag = cmd.getOptionValue("rmsdTag"); String oMol = cmd.getOptionValue("outputMol"); boolean doMirror = cmd.hasOption("mirror"); boolean doOptimize = !cmd.hasOption("doNotOptimize"); boolean quiet = cmd.hasOption("quiet"); OUTType outputMol = oMol == null ? OUTType.ALIGNED : OUTType.valueOf(oMol.toUpperCase()); if (atomMatch.startsWith("|none")) atomExpr = 0; if (atomMatch.contains("|hcount|")) atomExpr |= OEExprOpts.HCount; if (atomMatch.contains("|noAromatic|")) atomExpr &= (~OEExprOpts.Aromaticity); if (bondMatch.startsWith("|none")) bondExpr = 0; ArrayList<OEMol> refmols = new ArrayList<OEMol>(); if (refFile != null) { oemolistream reffs = new oemolistream(refFile); if (!is3DFormat(reffs.GetFormat())) oechem.OEThrow.Fatal("Invalid input format: need 3D coordinates"); reffs.SetFormat(OEFormat.MDL); int aromodel = OEIFlavor.Generic.OEAroModelOpenEye; int qflavor = reffs.GetFlavor(reffs.GetFormat()); reffs.SetFlavor(reffs.GetFormat(), (qflavor | aromodel)); OEMol rmol = new OEMol(); while (oechem.OEReadMDLQueryFile(reffs, rmol)) { if (!cmd.hasOption("keepCoreHydrogens")) oechem.OESuppressHydrogens(rmol); refmols.add(rmol); rmol = new OEMol(); } rmol.delete(); if (refmols.size() == 0) throw new Error("reference file had no entries"); reffs.close(); } oemolistream fitfs = new oemolistream(inFile); if (!is3DFormat(fitfs.GetFormat())) oechem.OEThrow.Fatal("Invalid input format: need 3D coordinates"); oemolostream ofs = new oemolostream(outFile); if (!is3DFormat(ofs.GetFormat())) oechem.OEThrow.Fatal("Invalid output format: need 3D coordinates"); AlignInterface aligner = null; OEGraphMol fitmol = new OEGraphMol(); if (oechem.OEReadMolecule(fitfs, fitmol)) { if (refmols.size() == 0) { OEMol rmol = new OEMol(fitmol); if (!cmd.hasOption("keepCoreHydrogens")) oechem.OESuppressHydrogens(rmol); refmols.add(rmol); } if ("sss".equalsIgnoreCase(method)) { aligner = new SSSAlign(refmols, outputMol, rmsdTag, doOptimize, doMirror, atomExpr, bondExpr, quiet); } else if ("clique".equalsIgnoreCase(method)) { aligner = new CliqueAlign(refmols, outputMol, rmsdTag, doOptimize, doMirror, atomExpr, bondExpr, quiet); } else if ("fss".equalsIgnoreCase(method)) { if (cmd.hasOption("atomMatch") || cmd.hasOption("bondMatch")) exitWithHelp("method fss does not support '-atomMatch' or '-bondMatch'", options); aligner = new FSSAlign(refmols, outputMol, rmsdTag, doOptimize, doMirror); } else { aligner = new McsAlign(refmols, outputMol, rmsdTag, doOptimize, doMirror, atomExpr, bondExpr, quiet); } do { aligner.align(fitmol); oechem.OEWriteMolecule(ofs, fitmol); } while (oechem.OEReadMolecule(fitfs, fitmol)); } fitmol.delete(); if (aligner != null) aligner.close(); for (OEMolBase mol : refmols) mol.delete(); fitfs.close(); ofs.close(); }
From source file:ch.epfl.lsir.xin.test.ItemBasedCFTest.java
/** * @param args//w ww .ja va 2s .com */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//ItemBasedCF"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File(".//conf//ItemBasedCF.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; double totalPrecision = 0; double totalRecall = 0; double totalMAP = 0; double totalNDCG = 0; double totalMRR = 0; double totalAUC = 0; int F = 5; logger.println(F + "- folder cross validation."); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { logger.println("Folder: " + folder); System.out.println("Folder: " + folder); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } trainRatingMatrix.calculateGlobalAverage(); trainRatingMatrix.calculateItemsMean(); RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a item based collaborative filtering recommendation model."); ItemBasedCF algo = new ItemBasedCF(trainRatingMatrix); algo.setLogger(logger); algo.build();//if read local model, no need to build the model algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); logger.flush(); //rating prediction accuracy double RMSE = 0; double MAE = 0; double precision = 0; double recall = 0; double map = 0; double ndcg = 0; double mrr = 0; double auc = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID()), false); if (prediction > algo.getMaxRating()) prediction = algo.getMaxRating(); if (prediction < algo.getMinRating()) prediction = algo.getMinRating(); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; System.out.println("Folder --- MAE: " + MAE + " RMSE: " + RMSE); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Folder --- MAE: " + MAE + " RMSE: " + RMSE); //ranking accuracy if (algo.getTopN() > 0) { HashMap<Integer, ArrayList<ResultUnit>> results = new HashMap<Integer, ArrayList<ResultUnit>>(); for (int i = 0; i < trainRatingMatrix.getRow(); i++) { // ArrayList<ResultUnit> rec = algo.getRecommendationList(i); // results.put(i, rec); ArrayList<ResultUnit> rec = algo.getRecommendationList(i); if (rec == null) continue; int total = testRatingMatrix.getUserRatingNumber(i); if (total == 0)//this user is ignored continue; results.put(i, rec); } RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix, trainRatingMatrix); precision = generator.getPrecisionN(); totalPrecision = totalPrecision + precision; recall = generator.getRecallN(); totalRecall = totalRecall + recall; map = generator.getMAPN(); totalMAP = totalMAP + map; ndcg = generator.getNDCGN(); totalNDCG = totalNDCG + ndcg; mrr = generator.getMRRN(); totalMRR = totalMRR + mrr; auc = generator.getAUC(); totalAUC = totalAUC + auc; System.out.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); logger.append("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc + "\n"); } } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); System.out.println("Precision@N: " + totalPrecision / F); System.out.println("Recall@N: " + totalRecall / F); System.out.println("MAP@N: " + totalMAP / F); System.out.println("MRR@N: " + totalMRR / F); System.out.println("NDCG@N: " + totalNDCG / F); System.out.println("AUC@N: " + totalAUC / F); System.out.println("similarity: " + config.getString("SIMILARITY")); //MAE: 0.7227232762922241 RMSE: 0.9225576790122603 (MovieLens 100K, shrinkage 2500, neighbor size 40, PCC) //MAE: 0.7250636319353241 RMSE: 0.9242305485411567 (MovieLens 100K, shrinkage 25, neighbor size 40, PCC) //MAE: 0.7477213243604459 RMSE: 0.9512195004171138 (MovieLens 100K, shrinkage 2500, neighbor size 40, COSINE) logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F + "\n" + "Precision@N: " + totalPrecision / F + "\n" + "Recall@N: " + totalRecall / F + "\n" + "MAP@N: " + totalMAP / F + "\n" + "MRR@N: " + totalMRR / F + "\n" + "NDCG@N: " + totalNDCG / F + "\n" + "AUC@N: " + totalAUC / F); logger.flush(); logger.close(); }
From source file:ch.epfl.lsir.xin.test.UserBasedCFTest.java
/** * @param args/*w w w . j a v a 2 s . c o m*/ */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//UserBasedCF"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File(".//conf//UserBasedCF.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; double totalPrecision = 0; double totalRecall = 0; double totalMAP = 0; double totalNDCG = 0; double totalMRR = 0; double totalAUC = 0; int F = 5; logger.println(F + "- folder cross validation."); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { logger.println("Folder: " + folder); System.out.println("Folder: " + folder); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } trainRatingMatrix.calculateGlobalAverage(); trainRatingMatrix.calculateUsersMean(); RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { // if( testRatings.get(i).getValue() < 5 ) // continue; testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } logger.println("Initialize a user based collaborative filtering recommendation model."); UserBasedCF algo = new UserBasedCF(trainRatingMatrix, false, ".//localModels//" + config.getString("NAME")); algo.setLogger(logger); algo.build();//if read local model, no need to build the model algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); System.out.println(trainRatings.size() + " vs. " + testRatings.size()); logger.flush(); //rating prediction accuracy double RMSE = 0; double MAE = 0; double precision = 0; double recall = 0; double map = 0; double ndcg = 0; double mrr = 0; double auc = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID()), false); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } if (prediction > algo.getMaxRating()) prediction = algo.getMaxRating(); if (prediction < algo.getMinRating()) prediction = algo.getMinRating(); MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; System.out.println("Folder --- MAE: " + MAE + " RMSE: " + RMSE); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Folder --- MAE: " + MAE + " RMSE: " + RMSE); logger.flush(); //ranking accuracy if (algo.getTopN() > 0) { HashMap<Integer, ArrayList<ResultUnit>> results = new HashMap<Integer, ArrayList<ResultUnit>>(); for (int i = 0; i < testRatingMatrix.getRow(); i++) { ArrayList<ResultUnit> rec = algo.getRecommendationList(i); if (rec == null) continue; int total = testRatingMatrix.getUserRatingNumber(i); if (total == 0)//this user is ignored continue; results.put(i, rec); // for( Map.Entry<Integer, Double> entry : testRatingMatrix.getRatingMatrix().get(i).entrySet() ) // { // System.out.print( entry.getKey() + "(" + entry.getValue() + ") , "); // } // System.out.println(); // for( int j = 0 ; j < rec.size() ; j++ ) // { // System.out.print(rec.get(j).getItemIndex() + "(" + rec.get(j).getPrediciton() + // ") , "); // } // System.out.println("**********"); } RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix, trainRatingMatrix); precision = generator.getPrecisionN(); totalPrecision = totalPrecision + precision; recall = generator.getRecallN(); totalRecall = totalRecall + recall; map = generator.getMAPN(); totalMAP = totalMAP + map; ndcg = generator.getNDCGN(); totalNDCG = totalNDCG + ndcg; mrr = generator.getMRRN(); totalMRR = totalMRR + mrr; auc = generator.getAUC(); totalAUC = totalAUC + auc; System.out.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); logger.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); } } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); System.out.println("Precision@N: " + totalPrecision / F); System.out.println("Recall@N: " + totalRecall / F); System.out.println("MAP@N: " + totalMAP / F); System.out.println("MRR@N: " + totalMRR / F); System.out.println("NDCG@N: " + totalNDCG / F); System.out.println("AUC@N: " + totalAUC / F); // MovieLens100k //MAE: 0.7343907480119425 RMSE: 0.9405808357192891 (MovieLens 100K, shrinkage 25, neighbor size 60, PCC) //MAE: 0.7522376630596646 RMSE: 0.9520931265724659 (MovieLens 100K, no shrinkage , neighbor size 40, COSINE) logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F + "\n" + "Precision@N: " + totalPrecision / F + "\n" + "Recall@N: " + totalRecall / F + "\n" + "MAP@N: " + totalMAP / F + "\n" + "MRR@N: " + totalMRR / F + "\n" + "NDCG@N: " + totalNDCG / F + "\n" + "AUC@N: " + totalAUC / F); logger.flush(); logger.close(); }
From source file:ch.epfl.lsir.xin.test.SVDPPTest.java
/** * @param args/*from w ww . j av a 2 s .co m*/ */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//SVDPP"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File("conf//SVDPlusPlus.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); logger.flush(); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; double totalPrecision = 0; double totalRecall = 0; double totalMAP = 0; double totalNDCG = 0; double totalMRR = 0; double totalAUC = 0; int F = 5; logger.println(F + "- folder cross validation."); logger.flush(); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { System.out.println("Folder: " + folder); logger.println("Folder: " + folder); logger.flush(); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { if (testRatings.get(i).getValue() < 5) continue; testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a SVD++ recommendation model."); logger.flush(); SVDPlusPlus algo = new SVDPlusPlus(trainRatingMatrix, false, ".//localModels//" + config.getString("NAME")); algo.setLogger(logger); algo.build(); algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); logger.flush(); //rating prediction accuracy double RMSE = 0; double MAE = 0; double precision = 0; double recall = 0; double map = 0; double ndcg = 0; double mrr = 0; double auc = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID()), false); if (prediction > algo.getMaxRating()) prediction = algo.getMaxRating(); if (prediction < algo.getMinRating()) prediction = algo.getMinRating(); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; System.out.println("Folder --- MAE: " + MAE + " RMSE: " + RMSE); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Folder --- MAE: " + MAE + " RMSE: " + RMSE); //ranking accuracy if (algo.getTopN() > 0) { HashMap<Integer, ArrayList<ResultUnit>> results = new HashMap<Integer, ArrayList<ResultUnit>>(); for (int i = 0; i < trainRatingMatrix.getRow(); i++) { ArrayList<ResultUnit> rec = algo.getRecommendationList(i); if (rec == null) continue; int total = testRatingMatrix.getUserRatingNumber(i); if (total == 0)//this user is ignored continue; results.put(i, rec); } RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix); precision = generator.getPrecisionN(); totalPrecision = totalPrecision + precision; recall = generator.getRecallN(); totalRecall = totalRecall + recall; map = generator.getMAPN(); totalMAP = totalMAP + map; ndcg = generator.getNDCGN(); totalNDCG = totalNDCG + ndcg; mrr = generator.getMRRN(); totalMRR = totalMRR + mrr; auc = generator.getAUC(); totalAUC = totalAUC + auc; System.out.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); logger.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); } logger.flush(); } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); System.out.println("Precision@N: " + totalPrecision / F); System.out.println("Recall@N: " + totalRecall / F); System.out.println("MAP@N: " + totalMAP / F); System.out.println("MRR@N: " + totalMRR / F); System.out.println("NDCG@N: " + totalNDCG / F); System.out.println("AUC@N: " + totalAUC / F); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F + "\n" + "Precision@N: " + totalPrecision / F + "\n" + "Recall@N: " + totalRecall / F + "\n" + "MAP@N: " + totalMAP / F + "\n" + "MRR@N: " + totalMRR / F + "\n" + "NDCG@N: " + totalNDCG / F + "\n" + "AUC@N: " + totalAUC / F); logger.flush(); logger.close(); }
From source file:metaTile.Main.java
/** * @param args//from w w w .ja v a 2 s . co m * @throws IOException */ public static void main(String[] args) throws IOException { try { /* parse the command line arguments */ // create the command line parser CommandLineParser parser = new PosixParser(); // create the Options Options options = new Options(); options.addOption("i", "input", true, "File to read original tile list from."); options.addOption("o", "output", true, "File to write shorter meta-tile list to."); options.addOption("m", "metatiles", true, "Number of tiles in x and y direction to group into one meta-tile."); // parse the command line arguments CommandLine commandLine = parser.parse(options, args); if (!commandLine.hasOption("input") || !commandLine.hasOption("output") || !commandLine.hasOption("metatiles")) printUsage(options); String inputFileName = commandLine.getOptionValue("input"); String outputFileName = commandLine.getOptionValue("output"); int metaTileSize = Integer.parseInt(commandLine.getOptionValue("metatiles")); ArrayList<RenderingTile> tiles = new ArrayList<RenderingTile>(); BufferedReader tileListReader = new BufferedReader(new FileReader(new File(inputFileName))); BufferedWriter renderMetatileListWriter = new BufferedWriter(new FileWriter(new File(outputFileName))); String line = tileListReader.readLine(); while (line != null) { String[] columns = line.split("/"); if (columns.length == 3) tiles.add(new RenderingTile(Integer.parseInt(columns[0]), Integer.parseInt(columns[1]), Integer.parseInt(columns[2]))); line = tileListReader.readLine(); } tileListReader.close(); int hits = 0; // tiles which we are already rendering as the top left corner of 4x4 metatiles HashSet<RenderingTile> whitelist = new HashSet<RenderingTile>(); // for each tile in the list see if it has a meta-tile in the whitelist already for (int i = 0; i < tiles.size(); i++) { boolean hit = false; // by default we aren't already rendering this tile as part of another metatile for (int dx = 0; dx < metaTileSize; dx++) { for (int dy = 0; dy < metaTileSize; dy++) { RenderingTile candidate = new RenderingTile(tiles.get(i).z, tiles.get(i).x - dx, tiles.get(i).y - dy); if (whitelist.contains(candidate)) { hit = true; // now exit the two for loops iterating over tiles inside a meta-tile dx = metaTileSize; dy = metaTileSize; } } } // if this tile doesn't already have a meta-tile in the whitelist, add it if (hit == false) { hits++; renderMetatileListWriter.write(tiles.get(i).toString() + "/" + metaTileSize + "\n"); whitelist.add(tiles.get(i)); } } renderMetatileListWriter.close(); System.out.println( "Reduced " + tiles.size() + " tiles into " + hits + " metatiles of size " + metaTileSize); } catch (Exception e) { e.printStackTrace(); } }
From source file:ch.epfl.lsir.xin.test.SocialRegTest.java
/** * @param args// ww w . j a v a 2 s. c om */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//SocialReg"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File("conf//SocialReg.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); logger.flush(); DataLoaderFile loader = new DataLoaderFile(".//data//Epinions-ratings.txt"); loader.readSimple(); //read social information loader.readRelation(".//data//Epinions-trust.txt"); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; double totalPrecision = 0; double totalRecall = 0; double totalMAP = 0; double totalNDCG = 0; double totalMRR = 0; double totalAUC = 0; int F = 5; logger.println(F + "- folder cross validation."); logger.flush(); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { System.out.println("Folder: " + folder); logger.println("Folder: " + folder); logger.flush(); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = dataset.getUserIDMapping(); HashMap<String, Integer> itemIDIndexMapping = dataset.getItemIDMapping(); // for( int i = 0 ; i < dataset.getUserIDs().size() ; i++ ) // { // userIDIndexMapping.put(dataset.getUserIDs().get(i), i); // } // for( int i = 0 ; i < dataset.getItemIDs().size() ; i++ ) // { // itemIDIndexMapping.put(dataset.getItemIDs().get(i) , i); // } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a social regularization recommendation model."); logger.flush(); SocialReg algo = new SocialReg(trainRatingMatrix, dataset.getRelationships(), false, ".//localModels//" + config.getString("NAME")); algo.setLogger(logger); algo.build(); algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); logger.flush(); System.out.println(trainRatings.size() + " vs. " + testRatings.size()); //rating prediction accuracy double RMSE = 0; double MAE = 0; double precision = 0; double recall = 0; double map = 0; double ndcg = 0; double mrr = 0; double auc = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID())); if (prediction > algo.getMaxRating()) prediction = algo.getMaxRating(); if (prediction < algo.getMinRating()) prediction = algo.getMinRating(); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; System.out.println("Folder --- MAE: " + MAE + " RMSE: " + RMSE); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Folder --- MAE: " + MAE + " RMSE: " + RMSE); //ranking accuracy // if( algo.getTopN() > 0 ) // { // HashMap<Integer , ArrayList<ResultUnit>> results = new HashMap<Integer , ArrayList<ResultUnit>>(); // for( int i = 0 ; i < trainRatingMatrix.getRow() ; i++ ) // { // ArrayList<ResultUnit> rec = algo.getRecommendationList(i); // results.put(i, rec); // } // RankResultGenerator generator = new RankResultGenerator(results , algo.getTopN() , testRatingMatrix); // precision = generator.getPrecisionN(); // totalPrecision = totalPrecision + precision; // recall = generator.getRecallN(); // totalRecall = totalRecall + recall; // map = generator.getMAPN(); // totalMAP = totalMAP + map; // ndcg = generator.getNDCGN(); // totalNDCG = totalNDCG + ndcg; // mrr = generator.getMRRN(); // totalMRR = totalMRR + mrr; // auc = generator.getAUC(); // totalAUC = totalAUC + auc; // System.out.println("Folder --- precision: " + precision + " recall: " + // recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); // logger.println("Folder --- precision: " + precision + " recall: " + // recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + // mrr + " auc: " + auc); // } logger.flush(); } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); System.out.println("Precision@N: " + totalPrecision / F); System.out.println("Recall@N: " + totalRecall / F); System.out.println("MAP@N: " + totalMAP / F); System.out.println("MRR@N: " + totalMRR / F); System.out.println("NDCG@N: " + totalNDCG / F); System.out.println("AUC@N: " + totalAUC / F); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F + "\n" + "Precision@N: " + totalPrecision / F + "\n" + "Recall@N: " + totalRecall / F + "\n" + "MAP@N: " + totalMAP / F + "\n" + "MRR@N: " + totalMRR / F + "\n" + "NDCG@N: " + totalNDCG / F + "\n" + "AUC@N: " + totalAUC / F); logger.flush(); logger.close(); }
From source file:ch.epfl.lsir.xin.test.BiasedMFTest.java
/** * @param args//from w ww. j ava 2s. c o m */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//BiasedMF"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File("conf//biasedMF.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); logger.flush(); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; double totalPrecision = 0; double totalRecall = 0; double totalMAP = 0; double totalNDCG = 0; double totalMRR = 0; double totalAUC = 0; int F = 5; logger.println(F + "- folder cross validation."); logger.flush(); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { System.out.println("Folder: " + folder); logger.println("Folder: " + folder); logger.flush(); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { // if( testRatings.get(i).getValue() < 5 ) // continue; testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a biased matrix factorization recommendation model."); logger.flush(); BiasedMF algo = new BiasedMF(trainRatingMatrix, false, ".//localModels//" + config.getString("NAME")); algo.setLogger(logger); algo.build(); algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); logger.flush(); //rating prediction accuracy double RMSE = 0; double MAE = 0; double precision = 0; double recall = 0; double map = 0; double ndcg = 0; double mrr = 0; double auc = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID()), false); if (prediction > algo.getMaxRating()) prediction = algo.getMaxRating(); if (prediction < algo.getMinRating()) prediction = algo.getMinRating(); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; System.out.println("Folder --- MAE: " + MAE + " RMSE: " + RMSE); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Folder --- MAE: " + MAE + " RMSE: " + RMSE); //ranking accuracy if (algo.getTopN() > 0) { HashMap<Integer, ArrayList<ResultUnit>> results = new HashMap<Integer, ArrayList<ResultUnit>>(); for (int i = 0; i < trainRatingMatrix.getRow(); i++) { ArrayList<ResultUnit> rec = algo.getRecommendationList(i); if (rec == null) continue; int total = testRatingMatrix.getUserRatingNumber(i); if (total == 0)//this user is ignored continue; results.put(i, rec); } RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix, trainRatingMatrix); precision = generator.getPrecisionN(); totalPrecision = totalPrecision + precision; recall = generator.getRecallN(); totalRecall = totalRecall + recall; map = generator.getMAPN(); totalMAP = totalMAP + map; ndcg = generator.getNDCGN(); totalNDCG = totalNDCG + ndcg; mrr = generator.getMRRN(); totalMRR = totalMRR + mrr; auc = generator.getAUC(); totalAUC = totalAUC + auc; System.out.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); logger.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); } logger.flush(); } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); System.out.println("Precision@N: " + totalPrecision / F); System.out.println("Recall@N: " + totalRecall / F); System.out.println("MAP@N: " + totalMAP / F); System.out.println("MRR@N: " + totalMRR / F); System.out.println("NDCG@N: " + totalNDCG / F); System.out.println("AUC@N: " + totalAUC / F); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F + "\n" + "Precision@N: " + totalPrecision / F + "\n" + "Recall@N: " + totalRecall / F + "\n" + "MAP@N: " + totalMAP / F + "\n" + "MRR@N: " + totalMRR / F + "\n" + "NDCG@N: " + totalNDCG / F + "\n" + "AUC@N: " + totalAUC / F); logger.flush(); logger.close(); }
From source file:ch.epfl.lsir.xin.test.MFTest.java
/** * @param args//ww w . j a v a 2 s. com */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub PrintWriter logger = new PrintWriter(".//results//MF"); PropertiesConfiguration config = new PropertiesConfiguration(); config.setFile(new File("conf//MF.properties")); try { config.load(); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Read rating data..."); logger.flush(); DataLoaderFile loader = new DataLoaderFile(".//data//MoveLens100k.txt"); loader.readSimple(); DataSetNumeric dataset = loader.getDataset(); System.out.println("Number of ratings: " + dataset.getRatings().size() + " Number of users: " + dataset.getUserIDs().size() + " Number of items: " + dataset.getItemIDs().size()); logger.println("Number of ratings: " + dataset.getRatings().size() + ", Number of users: " + dataset.getUserIDs().size() + ", Number of items: " + dataset.getItemIDs().size()); logger.flush(); double totalMAE = 0; double totalRMSE = 0; double totalPrecision = 0; double totalRecall = 0; double totalMAP = 0; double totalNDCG = 0; double totalMRR = 0; double totalAUC = 0; int F = 5; logger.println(F + "- folder cross validation."); logger.flush(); ArrayList<ArrayList<NumericRating>> folders = new ArrayList<ArrayList<NumericRating>>(); for (int i = 0; i < F; i++) { folders.add(new ArrayList<NumericRating>()); } while (dataset.getRatings().size() > 0) { int index = new Random().nextInt(dataset.getRatings().size()); int r = new Random().nextInt(F); folders.get(r).add(dataset.getRatings().get(index)); dataset.getRatings().remove(index); } for (int folder = 1; folder <= F; folder++) { System.out.println("Folder: " + folder); logger.println("Folder: " + folder); logger.flush(); ArrayList<NumericRating> trainRatings = new ArrayList<NumericRating>(); ArrayList<NumericRating> testRatings = new ArrayList<NumericRating>(); for (int i = 0; i < folders.size(); i++) { if (i == folder - 1)//test data { testRatings.addAll(folders.get(i)); } else {//training data trainRatings.addAll(folders.get(i)); } } //create rating matrix HashMap<String, Integer> userIDIndexMapping = new HashMap<String, Integer>(); HashMap<String, Integer> itemIDIndexMapping = new HashMap<String, Integer>(); for (int i = 0; i < dataset.getUserIDs().size(); i++) { userIDIndexMapping.put(dataset.getUserIDs().get(i), i); } for (int i = 0; i < dataset.getItemIDs().size(); i++) { itemIDIndexMapping.put(dataset.getItemIDs().get(i), i); } RatingMatrix trainRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < trainRatings.size(); i++) { trainRatingMatrix.set(userIDIndexMapping.get(trainRatings.get(i).getUserID()), itemIDIndexMapping.get(trainRatings.get(i).getItemID()), trainRatings.get(i).getValue()); } RatingMatrix testRatingMatrix = new RatingMatrix(dataset.getUserIDs().size(), dataset.getItemIDs().size()); for (int i = 0; i < testRatings.size(); i++) { // if( testRatings.get(i).getValue() < 5 ) // continue; testRatingMatrix.set(userIDIndexMapping.get(testRatings.get(i).getUserID()), itemIDIndexMapping.get(testRatings.get(i).getItemID()), testRatings.get(i).getValue()); } System.out.println("Training: " + trainRatingMatrix.getTotalRatingNumber() + " vs Test: " + testRatingMatrix.getTotalRatingNumber()); logger.println("Initialize a matrix factorization based recommendation model."); logger.flush(); MatrixFactorization algo = new MatrixFactorization(trainRatingMatrix, false, ".//localModels//" + config.getString("NAME")); algo.setLogger(logger); algo.build(); algo.saveModel(".//localModels//" + config.getString("NAME")); logger.println("Save the model."); logger.flush(); //rating prediction accuracy double RMSE = 0; double MAE = 0; double precision = 0; double recall = 0; double map = 0; double ndcg = 0; double mrr = 0; double auc = 0; int count = 0; for (int i = 0; i < testRatings.size(); i++) { NumericRating rating = testRatings.get(i); double prediction = algo.predict(userIDIndexMapping.get(rating.getUserID()), itemIDIndexMapping.get(rating.getItemID()), false); if (prediction > algo.getMaxRating()) prediction = algo.getMaxRating(); if (prediction < algo.getMinRating()) prediction = algo.getMinRating(); if (Double.isNaN(prediction)) { System.out.println("no prediction"); continue; } MAE = MAE + Math.abs(rating.getValue() - prediction); RMSE = RMSE + Math.pow((rating.getValue() - prediction), 2); count++; } MAE = MAE / count; RMSE = Math.sqrt(RMSE / count); totalMAE = totalMAE + MAE; totalRMSE = totalRMSE + RMSE; System.out.println("Folder --- MAE: " + MAE + " RMSE: " + RMSE); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " Folder --- MAE: " + MAE + " RMSE: " + RMSE); //ranking accuracy if (algo.getTopN() > 0) { HashMap<Integer, ArrayList<ResultUnit>> results = new HashMap<Integer, ArrayList<ResultUnit>>(); for (int i = 0; i < trainRatingMatrix.getRow(); i++) { ArrayList<ResultUnit> rec = algo.getRecommendationList(i); if (rec == null) continue; int total = testRatingMatrix.getUserRatingNumber(i); if (total == 0)//this user is ignored continue; results.put(i, rec); // for( Map.Entry<Integer, Double> entry : testRatingMatrix.getRatingMatrix().get(i).entrySet() ) // { // System.out.print( entry.getKey() + "(" + entry.getValue() + ") , "); // } // System.out.println(); // for( int j = 0 ; j < rec.size() ; j++ ) // { // System.out.print(rec.get(j).getItemIndex() + "(" + rec.get(j).getPrediciton() + // ") , "); // } // System.out.println("**********"); } RankResultGenerator generator = new RankResultGenerator(results, algo.getTopN(), testRatingMatrix, trainRatingMatrix); precision = generator.getPrecisionN(); totalPrecision = totalPrecision + precision; recall = generator.getRecallN(); totalRecall = totalRecall + recall; map = generator.getMAPN(); totalMAP = totalMAP + map; ndcg = generator.getNDCGN(); totalNDCG = totalNDCG + ndcg; mrr = generator.getMRRN(); totalMRR = totalMRR + mrr; auc = generator.getAUC(); totalAUC = totalAUC + auc; System.out.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); logger.println("Folder --- precision: " + precision + " recall: " + recall + " map: " + map + " ndcg: " + ndcg + " mrr: " + mrr + " auc: " + auc); } logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + " MAE: " + MAE + " RMSE: " + RMSE); logger.flush(); } System.out.println("MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F); System.out.println("Precision@N: " + totalPrecision / F); System.out.println("Recall@N: " + totalRecall / F); System.out.println("MAP@N: " + totalMAP / F); System.out.println("MRR@N: " + totalMRR / F); System.out.println("NDCG@N: " + totalNDCG / F); System.out.println("AUC@N: " + totalAUC / F); logger.println(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date()) + "\n" + "MAE: " + totalMAE / F + " RMSE: " + totalRMSE / F + "\n" + "Precision@N: " + totalPrecision / F + "\n" + "Recall@N: " + totalRecall / F + "\n" + "MAP@N: " + totalMAP / F + "\n" + "MRR@N: " + totalMRR / F + "\n" + "NDCG@N: " + totalNDCG / F + "\n" + "AUC@N: " + totalAUC / F); logger.flush(); logger.close(); }