List of usage examples for java.io PrintWriter close
public void close()
From source file:client.QueryLastFm.java
License:asdf
public static void main(String[] args) throws Exception { // isAlreadyInserted("asdfs","jas,jnjkah"); // FileWriter fw = new FileWriter(".\\tracks.csv"); OutputStream track_os = new FileOutputStream(".\\tracks.csv"); PrintWriter out = new PrintWriter(new OutputStreamWriter(track_os, "UTF-8")); OutputStream track_id_os = new FileOutputStream(".\\track_id_sim_track_id.csv"); PrintWriter track_id_out = new PrintWriter(new OutputStreamWriter(track_id_os, "UTF-8")); track_id_out.print(""); ByteArrayInputStream input;/* www .j a v a2s.c om*/ Document doc = null; CloseableHttpClient httpclient = HttpClients.createDefault(); String trackName = ""; String artistName = ""; String sourceMbid = ""; out.print("ID");// first row first column out.print(","); out.print("TrackName");// first row second column out.print(","); out.println("Artist");// first row third column track_id_out.print("source");// first row second column track_id_out.print(","); track_id_out.println("target");// first row third column // track_id_out.print(","); // track_id_out.println("type");// first row third column // out.flush(); // out.close(); // fw.close(); // os.close(); try { URI uri = new URIBuilder().setScheme("http").setHost("ws.audioscrobbler.com").setPath("/2.0/") .setParameter("method", "track.getsimilar").setParameter("artist", "cher") .setParameter("track", "believe").setParameter("limit", "100") .setParameter("api_key", "88858618961414f8bec919bddd057044").build(); // new URIBuilder(). HttpGet request = new HttpGet(uri); // request. // This is useful for last.fm logging and preventing them from blocking this client request.setHeader(HttpHeaders.USER_AGENT, "nileshmore@gatech.edu - ClassAssignment at GeorgiaTech Non-commercial use"); HttpGet httpGet = new HttpGet( "http://ws.audioscrobbler.com/2.0/?method=track.getsimilar&artist=cher&track=believe&limit=4&api_key=88858618961414f8bec919bddd057044"); CloseableHttpResponse response = httpclient.execute(request); int statusCode = response.getStatusLine().getStatusCode(); DocumentBuilderFactory factory = DocumentBuilderFactory.newInstance(); DocumentBuilder builder = factory.newDocumentBuilder(); // The underlying HTTP connection is still held by the response object // to allow the response content to be streamed directly from the network socket. // In order to ensure correct deallocation of system resources // the user MUST call CloseableHttpResponse#close() from a finally clause. // Please note that if response content is not fully consumed the underlying // connection cannot be safely re-used and will be shut down and discarded // by the connection manager. try { if (statusCode == 200) { HttpEntity entity1 = response.getEntity(); BufferedReader br = new BufferedReader( new InputStreamReader((response.getEntity().getContent()))); Document document = builder.parse((response.getEntity().getContent())); Element root = document.getDocumentElement(); root.normalize(); // Need to focus and resolve this part NodeList nodes; nodes = root.getChildNodes(); nodes = root.getElementsByTagName("track"); if (nodes.getLength() == 0) { // System.out.println("empty"); return; } Node trackNode; for (int k = 0; k < nodes.getLength(); k++) // can access all tracks now { trackNode = nodes.item(k); NodeList trackAttributes = trackNode.getChildNodes(); // check if mbid is present in track attributes // System.out.println("Length " + (trackAttributes.item(5).getNodeName().compareToIgnoreCase("mbid") == 0)); if ((trackAttributes.item(5).getNodeName().compareToIgnoreCase("mbid") == 0)) { if (((Element) trackAttributes.item(5)).hasChildNodes()) ;// System.out.println("Go aHead"); else continue; } else continue; for (int n = 0; n < trackAttributes.getLength(); n++) { Node attribute = trackAttributes.item(n); if ((attribute.getNodeName().compareToIgnoreCase("name")) == 0) { // System.out.println(((Element)attribute).getFirstChild().getNodeValue()); trackName = ((Element) attribute).getFirstChild().getNodeValue(); // make string encoding as UTF-8 ************ } if ((attribute.getNodeName().compareToIgnoreCase("mbid")) == 0) { // System.out.println(n + " " + ((Element)attribute).getFirstChild().getNodeValue()); sourceMbid = attribute.getFirstChild().getNodeValue(); } if ((attribute.getNodeName().compareToIgnoreCase("artist")) == 0) { NodeList ArtistNodeList = attribute.getChildNodes(); for (int j = 0; j < ArtistNodeList.getLength(); j++) { Node Artistnode = ArtistNodeList.item(j); if ((Artistnode.getNodeName().compareToIgnoreCase("name")) == 0) { // System.out.println(((Element)Artistnode).getFirstChild().getNodeValue()); artistName = ((Element) Artistnode).getFirstChild().getNodeValue(); } } } } out.print(sourceMbid); out.print(","); out.print(trackName); out.print(","); out.println(artistName); // out.print(","); findSimilarTracks(track_id_out, sourceMbid, trackName, artistName); } track_id_out.flush(); out.flush(); out.close(); track_id_out.close(); track_os.close(); // fw.close(); Element trac = (Element) nodes.item(0); // trac.normalize(); nodes = trac.getChildNodes(); // System.out.println(nodes.getLength()); for (int i = 0; i < nodes.getLength(); i++) { Node node = nodes.item(i); // System.out.println(node.getNodeName()); if ((node.getNodeName().compareToIgnoreCase("name")) == 0) { // System.out.println(((Element)node).getFirstChild().getNodeValue()); } if ((node.getNodeName().compareToIgnoreCase("mbid")) == 0) { // System.out.println(((Element)node).getFirstChild().getNodeValue()); } if ((node.getNodeName().compareToIgnoreCase("artist")) == 0) { // System.out.println("Well"); NodeList ArtistNodeList = node.getChildNodes(); for (int j = 0; j < ArtistNodeList.getLength(); j++) { Node Artistnode = ArtistNodeList.item(j); if ((Artistnode.getNodeName().compareToIgnoreCase("name")) == 0) { /* System.out.println(((Element)Artistnode).getFirstChild().getNodeValue());*/ } /*System.out.println(Artistnode.getNodeName());*/ } } } /*if(node instanceof Element){ //a child element to process Element child = (Element) node; String attribute = child.getAttribute("width"); }*/ // System.out.println(root.getAttribute("status")); NodeList tracks = root.getElementsByTagName("track"); Element track = (Element) tracks.item(0); // System.out.println(track.getTagName()); track.getChildNodes(); } else { System.out.println("failed with status" + response.getStatusLine()); } // input = (ByteArrayInputStream)entity1.getContent(); // do something useful with the response body // and ensure it is fully consumed } finally { response.close(); } } finally { System.out.println("Exited succesfully."); httpclient.close(); } }
From source file:edu.msu.cme.rdp.multicompare.Main.java
public static void main(String[] args) throws Exception { PrintStream hier_out = null;// www . j av a2s.c o m PrintWriter assign_out = new PrintWriter(new NullWriter()); PrintStream bootstrap_out = null; File hier_out_filename = null; String propFile = null; File biomFile = null; File metadataFile = null; PrintWriter shortseq_out = null; List<MCSample> samples = new ArrayList(); ClassificationResultFormatter.FORMAT format = ClassificationResultFormatter.FORMAT.allRank; float conf = CmdOptions.DEFAULT_CONF; String gene = null; int min_bootstrap_words = Classifier.MIN_BOOTSTRSP_WORDS; try { CommandLine line = new PosixParser().parse(options, args); if (line.hasOption(CmdOptions.OUTFILE_SHORT_OPT)) { assign_out = new PrintWriter(line.getOptionValue(CmdOptions.OUTFILE_SHORT_OPT)); } else { throw new IllegalArgumentException("Require the output file for classification assignment"); } if (line.hasOption(CmdOptions.HIER_OUTFILE_SHORT_OPT)) { hier_out_filename = new File(line.getOptionValue(CmdOptions.HIER_OUTFILE_SHORT_OPT)); hier_out = new PrintStream(hier_out_filename); } if (line.hasOption(CmdOptions.BIOMFILE_SHORT_OPT)) { biomFile = new File(line.getOptionValue(CmdOptions.BIOMFILE_SHORT_OPT)); } if (line.hasOption(CmdOptions.METADATA_SHORT_OPT)) { metadataFile = new File(line.getOptionValue(CmdOptions.METADATA_SHORT_OPT)); } if (line.hasOption(CmdOptions.TRAINPROPFILE_SHORT_OPT)) { if (gene != null) { throw new IllegalArgumentException( "Already specified the gene from the default location. Can not specify train_propfile"); } else { propFile = line.getOptionValue(CmdOptions.TRAINPROPFILE_SHORT_OPT); } } if (line.hasOption(CmdOptions.FORMAT_SHORT_OPT)) { String f = line.getOptionValue(CmdOptions.FORMAT_SHORT_OPT); if (f.equalsIgnoreCase("allrank")) { format = ClassificationResultFormatter.FORMAT.allRank; } else if (f.equalsIgnoreCase("fixrank")) { format = ClassificationResultFormatter.FORMAT.fixRank; } else if (f.equalsIgnoreCase("filterbyconf")) { format = ClassificationResultFormatter.FORMAT.filterbyconf; } else if (f.equalsIgnoreCase("db")) { format = ClassificationResultFormatter.FORMAT.dbformat; } else if (f.equalsIgnoreCase("biom")) { format = ClassificationResultFormatter.FORMAT.biom; } else { throw new IllegalArgumentException( "Not an valid output format, only allrank, fixrank, biom, filterbyconf and db allowed"); } } if (line.hasOption(CmdOptions.GENE_SHORT_OPT)) { if (propFile != null) { throw new IllegalArgumentException( "Already specified train_propfile. Can not specify gene any more"); } gene = line.getOptionValue(CmdOptions.GENE_SHORT_OPT).toLowerCase(); if (!gene.equals(ClassifierFactory.RRNA_16S_GENE) && !gene.equals(ClassifierFactory.FUNGALLSU_GENE) && !gene.equals(ClassifierFactory.FUNGALITS_warcup_GENE) && !gene.equals(ClassifierFactory.FUNGALITS_unite_GENE)) { throw new IllegalArgumentException(gene + " not found, choose from" + ClassifierFactory.RRNA_16S_GENE + ", " + ClassifierFactory.FUNGALLSU_GENE + ", " + ClassifierFactory.FUNGALITS_warcup_GENE + ", " + ClassifierFactory.FUNGALITS_unite_GENE); } } if (line.hasOption(CmdOptions.MIN_BOOTSTRAP_WORDS_SHORT_OPT)) { min_bootstrap_words = Integer .parseInt(line.getOptionValue(CmdOptions.MIN_BOOTSTRAP_WORDS_SHORT_OPT)); if (min_bootstrap_words < Classifier.MIN_BOOTSTRSP_WORDS) { throw new IllegalArgumentException(CmdOptions.MIN_BOOTSTRAP_WORDS_LONG_OPT + " must be at least " + Classifier.MIN_BOOTSTRSP_WORDS); } } if (line.hasOption(CmdOptions.BOOTSTRAP_SHORT_OPT)) { String confString = line.getOptionValue(CmdOptions.BOOTSTRAP_SHORT_OPT); try { conf = Float.valueOf(confString); } catch (NumberFormatException e) { throw new IllegalArgumentException("Confidence must be a decimal number"); } if (conf < 0 || conf > 1) { throw new IllegalArgumentException("Confidence must be in the range [0,1]"); } } if (line.hasOption(CmdOptions.SHORTSEQ_OUTFILE_SHORT_OPT)) { shortseq_out = new PrintWriter(line.getOptionValue(CmdOptions.SHORTSEQ_OUTFILE_SHORT_OPT)); } if (line.hasOption(CmdOptions.BOOTSTRAP_OUTFILE_SHORT_OPT)) { bootstrap_out = new PrintStream(line.getOptionValue(CmdOptions.BOOTSTRAP_OUTFILE_SHORT_OPT)); } if (format.equals(ClassificationResultFormatter.FORMAT.biom) && biomFile == null) { throw new IllegalArgumentException("biom format requires an input biom file"); } if (biomFile != null) { // if input biom file provided, use biom format format = ClassificationResultFormatter.FORMAT.biom; } args = line.getArgs(); for (String arg : args) { String[] inFileNames = arg.split(","); File inputFile = new File(inFileNames[0]); File idmappingFile = null; if (!inputFile.exists()) { throw new IllegalArgumentException("Failed to find input file \"" + inFileNames[0] + "\""); } if (inFileNames.length == 2) { idmappingFile = new File(inFileNames[1]); if (!idmappingFile.exists()) { throw new IllegalArgumentException("Failed to find input file \"" + inFileNames[1] + "\""); } } MCSample nextSample = new MCSample(inputFile, idmappingFile); samples.add(nextSample); } if (propFile == null && gene == null) { gene = CmdOptions.DEFAULT_GENE; } if (samples.size() < 1) { throw new IllegalArgumentException("Require at least one sample files"); } } catch (Exception e) { System.out.println("Command Error: " + e.getMessage()); new HelpFormatter().printHelp(80, " [options] <samplefile>[,idmappingfile] ...", "", options, ""); return; } MultiClassifier multiClassifier = new MultiClassifier(propFile, gene, biomFile, metadataFile); MultiClassifierResult result = multiClassifier.multiCompare(samples, conf, assign_out, format, min_bootstrap_words); assign_out.close(); if (hier_out != null) { DefaultPrintVisitor printVisitor = new DefaultPrintVisitor(hier_out, samples); result.getRoot().topDownVisit(printVisitor); hier_out.close(); if (multiClassifier.hasCopyNumber()) { // print copy number corrected counts File cn_corrected_s = new File(hier_out_filename.getParentFile(), "cnadjusted_" + hier_out_filename.getName()); PrintStream cn_corrected_hier_out = new PrintStream(cn_corrected_s); printVisitor = new DefaultPrintVisitor(cn_corrected_hier_out, samples, true); result.getRoot().topDownVisit(printVisitor); cn_corrected_hier_out.close(); } } if (bootstrap_out != null) { for (MCSample sample : samples) { MCSamplePrintUtil.printBootstrapCountTable(bootstrap_out, sample); } bootstrap_out.close(); } if (shortseq_out != null) { for (String id : result.getBadSequences()) { shortseq_out.write(id + "\n"); } shortseq_out.close(); } }
From source file:ch.epfl.lsir.xin.test.SVDPPTest.java
/** * @param args//from w w w. j a v a2 s .c o 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:ch.epfl.lsir.xin.test.BiasedMFTest.java
/** * @param args//from w w w . jav a 2s . c om */ 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.SocialRegTest.java
/** * @param args/*from ww w . ja va2 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.ItemBasedCFTest.java
/** * @param args//from w w w.ja va2s . co m */ 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:Main.java
public static void writeToFile(File file, String content) { try {//from www .j av a 2 s.co m PrintWriter out = new PrintWriter(file); out.println(content); out.close(); } catch (FileNotFoundException e) { e.printStackTrace(); } }
From source file:Main.java
/** * Writes a DOM node (and all its ancestors) to the given output stream * //from ww w .j ava 2 s . co m * @param n * the node to write * @param o * the output stream to write the node to */ public static void writeNode(Node n, FileOutputStream o) { PrintWriter w = new PrintWriter(o); w.print(n.toString()); w.close(); }
From source file:Main.java
public static void clearLog() { File logFile = new File(Environment.getExternalStorageDirectory() + "/clr_log.file"); PrintWriter writer; try {//w w w. j a v a 2 s . c o m writer = new PrintWriter(logFile); writer.close(); } catch (Exception e) { e.printStackTrace(); } }
From source file:Main.java
public static String getErrorInfo(Throwable arg1) { Writer writer = new StringWriter(); PrintWriter pw = new PrintWriter(writer); arg1.printStackTrace(pw);/*from www .j av a 2 s . co m*/ pw.close(); String error = writer.toString(); return error; }