List of usage examples for org.apache.mahout.cf.taste.recommender Recommender estimatePreference
float estimatePreference(long userID, long itemID) throws TasteException;
From source file:com.msiiplab.recsys.rwr.GLRecommenderIRStatsEvaluator.java
License:Apache License
public void predict(RecommenderBuilder recommenderBuilder, List<DataModel> trainingDataModels, List<DataModel> testingDataModels, List<File> outputFileList) throws TasteException { // num of train/test pair: num of cross validation folds int numFolds = trainingDataModels.size(); for (int i_folds = 0; i_folds < numFolds; i_folds++) { DataModel trainDataModel = trainingDataModels.get(i_folds); DataModel testDataModel = testingDataModels.get(i_folds); // Build recommender Recommender recommender = recommenderBuilder.buildRecommender(trainDataModel); LongPrimitiveIterator it_item;//from www. j a va2 s. co m FastIDSet allItems = new FastIDSet(); it_item = trainDataModel.getItemIDs(); while (it_item.hasNext()) { allItems.add(it_item.nextLong()); } it_item = testDataModel.getItemIDs(); while (it_item.hasNext()) { allItems.add(it_item.nextLong()); } if (outputFileList != null) { File file = outputFileList.get(i_folds); log.info("Writing recommender output to file: " + file.getPath()); try { FileOutputStream out = new FileOutputStream(file); LongPrimitiveIterator it_user = testDataModel.getUserIDs(); while (it_user.hasNext()) { long userID = it_user.nextLong(); FastIDSet ratedItems = trainDataModel.getItemIDsFromUser(userID); StringBuilder sb = new StringBuilder(); Iterator<Long> it = allItems.iterator(); while (it.hasNext()) { long itemID = it.next(); if (!ratedItems.contains(itemID)) { float pref = 0; try { pref = recommender.estimatePreference(userID, itemID); } catch (NoSuchItemException e) { pref = Float.NaN; } sb.append(userID + "," + itemID + "," + pref + "\n"); } } out.write(sb.toString().getBytes()); out.flush(); } out.close(); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } } } }
From source file:com.msiiplab.recsys.rwr.GLRecommenderIRStatsEvaluator.java
License:Apache License
public GLIRStatisticsImpl evaluate(RecommenderBuilder recommenderBuilder, List<DataModel> trainingDataModels, List<DataModel> testingDataModels, IDRescorer rescorer, int at, double relevanceThreshold, double evaluationPercentage) throws TasteException { Preconditions.checkArgument(recommenderBuilder != null, "recommenderBuilder is null"); Preconditions.checkArgument(trainingDataModels != null, "trainingDataModels is null"); Preconditions.checkArgument(testingDataModels != null, "testingDataModels is null"); Preconditions.checkArgument(testingDataModels.size() == trainingDataModels.size(), "trainingDataModels.size must equals testingDataModels.size"); Preconditions.checkArgument(at >= 1, "at must be at least 1"); Preconditions.checkArgument(evaluationPercentage > 0.0 && evaluationPercentage <= 1.0, "Invalid evaluationPercentage: %s", evaluationPercentage); // num of train/test pair: num of cross validation folds int numFolds = trainingDataModels.size(); RunningAverage CrossValidationPrecision = new GLRunningAverage(); RunningAverage CrossValidationRPrecision = new GLRunningAverage(); RunningAverage CrossValidationRecall = new GLRunningAverage(); RunningAverage CrossValidationFallOut = new GLRunningAverage(); RunningAverage CrossValidationNDCG = new GLRunningAverage(); RunningAverage CrossValidationRNDCG = new GLRunningAverage();//rating-nDCG RunningAverage CrossValidationReach = new GLRunningAverage(); RunningAverage CrossValidationMacroDOA = new GLRunningAverage(); RunningAverage CrossValidationMicroDOA = new GLRunningAverage(); RunningAverage CrossValidationMacroInnerDOA = new GLRunningAverage(); RunningAverage CrossValidationMicroInnerDOA = new GLRunningAverage(); for (int i_folds = 0; i_folds < numFolds; i_folds++) { log.info("fold {}", i_folds); DataModel trainDataModel = trainingDataModels.get(i_folds); DataModel testDataModel = testingDataModels.get(i_folds); FastIDSet MovieIDs = new FastIDSet(); LongPrimitiveIterator it_train_temp = trainDataModel.getItemIDs(); LongPrimitiveIterator it_test_temp = testDataModel.getItemIDs(); while (it_train_temp.hasNext()) { MovieIDs.add(it_train_temp.nextLong()); }/* w w w . j a va 2 s . c om*/ while (it_test_temp.hasNext()) { MovieIDs.add(it_test_temp.nextLong()); } int numTrainItems = trainDataModel.getNumItems(); int numTestItems = testDataModel.getNumItems(); int numItems = numTestItems + numTrainItems; RunningAverage precision = new GLRunningAverage(); RunningAverage rPrecision = new GLRunningAverage(); RunningAverage recall = new GLRunningAverage(); RunningAverage fallOut = new GLRunningAverage(); RunningAverage nDCG = new GLRunningAverage(); RunningAverage rNDCG = new GLRunningAverage(); RunningAverage macroDOA = new GLRunningAverage(); RunningAverage microDOA1 = new GLRunningAverage(); RunningAverage microDOA2 = new GLRunningAverage(); RunningAverage macroInnerDOA = new GLRunningAverage(); RunningAverage microInnerDOA1 = new GLRunningAverage(); RunningAverage microInnerDOA2 = new GLRunningAverage(); int numUsersRecommendedFor = 0; int numUsersWithRecommendations = 0; long start = System.currentTimeMillis(); // Build recommender Recommender recommender = recommenderBuilder.buildRecommender(trainDataModel); LongPrimitiveIterator it_user = testDataModel.getUserIDs(); while (it_user.hasNext()) { long userID = it_user.nextLong(); log.info("user {}", userID); // Use all in testDataModel as relevant FastIDSet learnedItemIDs; FastIDSet relevantItemIDs; try { learnedItemIDs = trainDataModel.getItemIDsFromUser(userID); relevantItemIDs = testDataModel.getItemIDsFromUser(userID); } catch (NoSuchUserException e1) { continue; } // We excluded zero relevant items situation int numRelevantItems = relevantItemIDs.size(); if (numRelevantItems <= 0) { continue; } // We excluded all prefs for the user that has no pref record in // training set try { trainDataModel.getPreferencesFromUser(userID); } catch (NoSuchUserException nsee) { continue; // Oops we excluded all prefs for the user -- just // move on } // Recommend items List<RecommendedItem> recommendedItems = recommender.recommend(userID, at, rescorer); List<RecommendedItem> recommendedItemsAtRelNum = recommender.recommend(userID, numRelevantItems, rescorer); PreferenceArray userPreferences = testDataModel.getPreferencesFromUser(userID); FastByIDMap<Preference> userPreferenceMap = getPrefereceMap(userPreferences); userPreferences.sortByValueReversed(); // relevantItemIDsAtN only consider top N items as relevant items FastIDSet relevantItemIDsAtN = new FastIDSet(); Iterator<Preference> it_pref = userPreferences.iterator(); int num_pref = 0; while (it_pref.hasNext()) { relevantItemIDsAtN.add(it_pref.next().getItemID()); num_pref++; if (num_pref >= at) { break; } } // Compute intersection between recommended items and relevant // items int intersectionSize = 0; int numRecommendedItems = recommendedItems.size(); for (RecommendedItem recommendedItem : recommendedItems) { if (relevantItemIDs.contains(recommendedItem.getItemID())) { intersectionSize++; } } // Precision double prec = 0; if (numRecommendedItems > 0) { prec = (double) intersectionSize / (double) numRecommendedItems; } precision.addDatum(prec); log.info("Precision for user {} is {}", userID, prec); // Recall double rec = (double) intersectionSize / (double) numRelevantItems; recall.addDatum(rec); log.info("Recall for user {} is {}", userID, rec); // R-precision double rprec = 0; int intersectionSizeAtRelNum = 0; int numRecommendedItemsAtRelNum = recommendedItemsAtRelNum.size(); for (RecommendedItem recommendedItem : recommendedItemsAtRelNum) { if (relevantItemIDs.contains(recommendedItem.getItemID())) { intersectionSizeAtRelNum++; } } if (numRecommendedItemsAtRelNum > 0) { rprec = (double) intersectionSizeAtRelNum / (double) numRelevantItems; } rPrecision.addDatum(rprec); log.info("RPrecision for user {} is {}", userID, rprec); double F1 = 0; if (prec + rec > 0) { F1 = 2 * prec * rec / (prec + rec); } log.info("F1 for user {} is {}", userID, F1); // Fall-out double fall = 0; int size = numRelevantItems + trainDataModel.getItemIDsFromUser(userID).size(); if (numRelevantItems < size) { fall = (double) (numRecommendedItems - intersectionSize) / (double) (numItems - numRelevantItems); } fallOut.addDatum(fall); log.info("Fallout for user {} is {}", userID, fall); // nDCG // In computing, assume relevant IDs have relevance ${rating} and others // 0 PreferenceArray userPredictions = getPreferenceArray(recommendedItems, userID); double userNDCG = computeNDCG(userPreferences, userPredictions, relevantItemIDs, userPreferenceMap, at); double userRNDCG = computeRNDCG(userPreferences, userPredictions, relevantItemIDs, userPreferenceMap, at); nDCG.addDatum(userNDCG); rNDCG.addDatum(userRNDCG); log.info("NDCG for user {} is {}", userID, userNDCG); log.info("RNDCG for user {} is {}", userID, userRNDCG); // Reach numUsersRecommendedFor++; if (numRecommendedItems > 0) { numUsersWithRecommendations++; } // DOA // [Siegel and Castellan, 1988] and [Gori and Pucci, 2007] // LongPrimitiveIterator it_movies = MovieIDs.iterator(); LongPrimitiveIterator it_movies = trainDataModel.getItemIDs(); long numNW = 0; long sumCheckOrder = 0; while (it_movies.hasNext()) { long itemID = it_movies.nextLong(); if (!learnedItemIDs.contains(itemID) && !relevantItemIDs.contains(itemID)) { // itemID is in NW_{u_i} numNW++; LongPrimitiveIterator it_test = relevantItemIDs.iterator(); while (it_test.hasNext()) { long testItemID = it_test.nextLong(); float itemPref = 0; float testItemPref = 0; try { itemPref = recommender.estimatePreference(userID, itemID); } catch (NoSuchItemException e) { } try { testItemPref = recommender.estimatePreference(userID, testItemID); } catch (NoSuchItemException e) { } if (itemPref <= testItemPref) { sumCheckOrder++; } } } } if (numNW > 0 && relevantItemIDs.size() > 0) { macroDOA.addDatum((double) sumCheckOrder / (double) (relevantItemIDs.size() * numNW)); microDOA1.addDatum((double) sumCheckOrder); microDOA2.addDatum((double) (relevantItemIDs.size() * numNW)); } // log.info( // "sumCheckOrder / (numNW * numRelevant) = {} / ({} * {})", // sumCheckOrder, numNW, relevantItemIDs.size()); // InnerDOA: only check the agreement of order in test set LongPrimitiveIterator it_test1 = relevantItemIDs.iterator(); long sumCheckInnerOrder = 0; long sumAll = 0; while (it_test1.hasNext()) { long itemID1 = it_test1.nextLong(); LongPrimitiveIterator it_test2 = relevantItemIDs.iterator(); while (it_test2.hasNext()) { long itemID2 = it_test2.nextLong(); if (itemID1 != itemID2) { try { float pref_v1 = testDataModel.getPreferenceValue(userID, itemID1); float pref_v2 = testDataModel.getPreferenceValue(userID, itemID2); float predict_v1 = recommender.estimatePreference(userID, itemID1); float predict_v2 = recommender.estimatePreference(userID, itemID2); if ((pref_v1 >= pref_v2 && predict_v1 >= predict_v2) || (pref_v1 <= pref_v2 && predict_v1 <= predict_v2)) { sumCheckInnerOrder++; } sumAll++; } catch (NoSuchItemException e) { // do nothing, just ignore } } } } if (relevantItemIDs.size() > 1) { macroInnerDOA.addDatum((double) sumCheckInnerOrder / (double) sumAll); microInnerDOA1.addDatum((double) sumCheckInnerOrder); microInnerDOA2.addDatum((double) sumAll); } // log.info( // "sumCheckInnerOrder / (|T| * (|T|-1) ) = {} / ({} * {}) = ", // sumCheckInnerOrder, relevantItemIDs.size(), relevantItemIDs.size()-1); } long end = System.currentTimeMillis(); CrossValidationPrecision.addDatum(precision.getAverage()); CrossValidationRPrecision.addDatum(rPrecision.getAverage()); CrossValidationRecall.addDatum(recall.getAverage()); CrossValidationFallOut.addDatum(fallOut.getAverage()); CrossValidationNDCG.addDatum(nDCG.getAverage()); CrossValidationRNDCG.addDatum(rNDCG.getAverage()); CrossValidationReach.addDatum((double) numUsersWithRecommendations / (double) numUsersRecommendedFor); CrossValidationMacroDOA.addDatum(macroDOA.getAverage()); CrossValidationMicroDOA.addDatum(microDOA1.getAverage() / microDOA2.getAverage()); CrossValidationMacroInnerDOA.addDatum(macroInnerDOA.getAverage()); CrossValidationMicroInnerDOA.addDatum(microInnerDOA1.getAverage() / microInnerDOA2.getAverage()); log.info("Evaluated with training/testing set # {} in {}ms", i_folds, end - start); System.out.printf("Evaluated with training/testing set # %d in %d ms \n", i_folds, end - start); log.info( "Precision/R-Precision/recall/fall-out/nDCG/rNDCG/reach/macroDOA/microDOA/macroInnerDOA/microInnerDOA: {} / {} / {} / {} / {} / {} / {} / {} / {} / {} / {}", precision.getAverage(), rPrecision.getAverage(), recall.getAverage(), fallOut.getAverage(), nDCG.getAverage(), rNDCG.getAverage(), (double) numUsersWithRecommendations / (double) numUsersRecommendedFor, macroDOA.getAverage(), microDOA1.getAverage() / microDOA2.getAverage(), macroInnerDOA.getAverage(), microInnerDOA1.getAverage() / microInnerDOA2.getAverage()); System.out.printf( "Precision/R-Precision/recall/fall-out/nDCG/rNDCG/reach/macroDOA/microDOA/macroInnerDOA/microInnerDOA: %f / %f / %f / %f / %f / %f / %f / %f / %f / %f / %f \n", precision.getAverage(), rPrecision.getAverage(), recall.getAverage(), fallOut.getAverage(), nDCG.getAverage(), rNDCG.getAverage(), (double) numUsersWithRecommendations / (double) numUsersRecommendedFor, macroDOA.getAverage(), microDOA1.getAverage() / microDOA2.getAverage(), macroInnerDOA.getAverage(), microInnerDOA1.getAverage() / microInnerDOA2.getAverage()); } log.info( "Cross Validation Precision/R-Precision/recall/fall-out/nDCG/rNDCG/reach/macroDOA/microDOA: {} / {} / {} / {} / {} / {} / {} / {} / {} / {} / {}", CrossValidationPrecision.getAverage(), CrossValidationRPrecision.getAverage(), CrossValidationRecall.getAverage(), CrossValidationFallOut.getAverage(), CrossValidationNDCG.getAverage(), CrossValidationRNDCG.getAverage(), CrossValidationReach.getAverage(), CrossValidationMacroDOA.getAverage(), CrossValidationMicroDOA.getAverage(), CrossValidationMacroInnerDOA.getAverage(), CrossValidationMicroInnerDOA.getAverage()); System.out.printf( "Cross Validation: \nPrecision/R-Precision/recall/fall-out/nDCG/rNDCG/reach/macroDOA/microDOA: %f / %f / %f / %f / %f / %f / %f / %f / %f / %f / %f\n", CrossValidationPrecision.getAverage(), CrossValidationRPrecision.getAverage(), CrossValidationRecall.getAverage(), CrossValidationFallOut.getAverage(), CrossValidationNDCG.getAverage(), CrossValidationRNDCG.getAverage(), CrossValidationReach.getAverage(), CrossValidationMacroDOA.getAverage(), CrossValidationMicroDOA.getAverage(), CrossValidationMacroInnerDOA.getAverage(), CrossValidationMicroInnerDOA.getAverage()); return new GLIRStatisticsImpl(CrossValidationPrecision.getAverage(), CrossValidationRPrecision.getAverage(), CrossValidationRecall.getAverage(), CrossValidationFallOut.getAverage(), CrossValidationNDCG.getAverage(), CrossValidationRNDCG.getAverage(), CrossValidationReach.getAverage(), CrossValidationMacroDOA.getAverage(), CrossValidationMicroDOA.getAverage(), CrossValidationMacroInnerDOA.getAverage(), CrossValidationMicroInnerDOA.getAverage()); }
From source file:lib.eval.AbstractDifferenceRecommenderEvaluator.java
License:Apache License
protected void evaluateOneUser(Recommender recommender, long testUserID, PreferenceArray prefs, AtomicInteger noEstimateCounter) throws TasteException { for (Preference realPref : prefs) { float estimatedPreference = Float.NaN; try {/*w w w . j a v a 2s.c o m*/ estimatedPreference = recommender.estimatePreference(testUserID, realPref.getItemID()); } catch (NoSuchUserException nsue) { // It's possible that an item exists in the test data but not training data in which case // NSEE will be thrown. Just ignore it and move on. log.info("User exists in test data but not training data: {}", testUserID); } catch (NoSuchItemException nsie) { log.info("Item exists in test data but not training data: {}", realPref.getItemID()); } if (Float.isNaN(estimatedPreference)) { noEstimateCounter.incrementAndGet(); } else { estimatedPreference = capEstimatedPreference(estimatedPreference); processOneEstimate(estimatedPreference, realPref); } } }
From source file:norbert.mynemo.core.evaluation.PersonnalRecommenderEvaluator.java
License:Apache License
/** * Returns the absolute difference between the given preference and the estimated one. Depending * on the recommender, the returned float may be NaN. *///www .java 2 s. c om private float getError(DataModel trainingModel, Recommender recommender, Preference preference) throws TasteException { float estimation = Float.NaN; predictionRequestNumber++; try { // may return Float.NaN estimation = capEstimatedPreference(recommender.estimatePreference(targetUser, preference.getItemID()), trainingModel); } catch (NoSuchUserException | NoSuchItemException exception) { // It's possible that an item exists in the test data but not // training data in which case NSEE or NSIE will be thrown. } return Math.abs(estimation - preference.getValue()); }