List of usage examples for java.lang Double isNaN
public static boolean isNaN(double v)
From source file:ch.epfl.lsir.xin.test.SVDPPTest.java
/** * @param args//ww w.j a v a 2s .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:ch.epfl.lsir.xin.test.BiasedMFTest.java
/** * @param args//www .ja v a 2 s . 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.MFTest.java
/** * @param args//from w w w.j a v a2 s. co m */ 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(); }
From source file:ch.epfl.lsir.xin.test.SocialRegTest.java
/** * @param args/* ww w. j a v a2s . c o m*/ */ 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:Stats.java
/** * Runs through some utils using the functions defined in this class. * /*from w w w .j a v a2 s . c o m*/ * @throws java.io.IOException */ public static void main(String[] args) throws IOException { double[] d = new double[0]; double dd = mean(d); System.out.println(dd + "\t" + Double.isNaN(dd)); for (int i = 0; i < 3; i++) { double[] x = new double[i]; System.out.println(mean(x) + "\t " + stderr(x) + "\t " + sdev(x)); } }
From source file:Main.java
/** * Returns true if the number is not NaN or infinite. * @param d//from ww w . j a v a 2 s . co m * @return */ public static boolean isReal(double d) { return !Double.isNaN(d) && !Double.isInfinite(d); }
From source file:Main.java
/** * Returns next bigger double value considering precision of the argument. * // www . java 2 s .c om */ public static double nextUp(double d) { if (Double.isNaN(d) || d == Double.POSITIVE_INFINITY) { return d; } else { d += 0.0; return Double.longBitsToDouble(Double.doubleToRawLongBits(d) + ((d >= 0.0) ? +1 : -1)); } }
From source file:Main.java
/** * Returns next smaller float value considering precision of the argument. * /*from w w w . ja va 2 s . co m*/ */ public static double nextDown(double d) { if (Double.isNaN(d) || d == Double.NEGATIVE_INFINITY) { return d; } else { if (d == 0.0f) { return -Float.MIN_VALUE; } else { return Double.longBitsToDouble(Double.doubleToRawLongBits(d) + ((d > 0.0f) ? -1 : +1)); } } }
From source file:Main.java
public static void normalize2(double[] x) { double sum = 0; for (int i = 0; i < x.length; i++) if (!Double.isNaN(x[i])) sum += x[i] * x[i];/* w ww. j av a 2 s . c o m*/ if (sum == 0) return; double f = 1.0 / Math.sqrt(sum); for (int i = 0; i < x.length; i++) if (!Double.isNaN(x[i])) x[i] *= f; }
From source file:Main.java
public static void normalizeByAvg(double[] x) { double sum = 0; int n = 0;/*from ww w .jav a2 s. co m*/ for (int i = 0; i < x.length; i++) if (!Double.isNaN(x[i])) { sum += x[i]; n++; } if (sum == 0) return; sum /= n; for (int i = 0; i < x.length; i++) if (!Double.isNaN(x[i])) x[i] -= sum; }