Exemplo n.º 1
0
 public CachingUserNeighborhood(UserNeighborhood neighborhood, DataModel dataModel)
     throws TasteException {
   Preconditions.checkArgument(neighborhood != null, "neighborhood is null");
   this.neighborhood = neighborhood;
   int maxCacheSize = dataModel.getNumUsers(); // just a dumb heuristic for sizing
   this.neighborhoodCache =
       new Cache<Long, long[]>(new NeighborhoodRetriever(neighborhood), maxCacheSize);
 }
Exemplo n.º 2
0
 /**
  * Exports the simple user IDs and associated item IDs in the data model.
  *
  * @return a {@link FastByIDMap} mapping user IDs to {@link FastIDSet}s representing that user's
  *     associated items
  */
 public static FastByIDMap<FastIDSet> toDataMap(DataModel dataModel) throws TasteException {
   FastByIDMap<FastIDSet> data = new FastByIDMap<>(dataModel.getNumUsers());
   LongPrimitiveIterator it = dataModel.getUserIDs();
   while (it.hasNext()) {
     long userID = it.nextLong();
     data.put(userID, dataModel.getItemIDsFromUser(userID));
   }
   return data;
 }
 @Override
 public double itemSimilarity(long itemID1, long itemID2) throws TasteException {
   int preferring1and2 = dataModel.getNumUsersWithPreferenceFor(itemID1, itemID2);
   if (preferring1and2 == 0) {
     return Double.NaN;
   }
   int preferring1 = dataModel.getNumUsersWithPreferenceFor(itemID1);
   int preferring2 = dataModel.getNumUsersWithPreferenceFor(itemID2);
   int numUsers = dataModel.getNumUsers();
   double logLikelihood =
       twoLogLambda(
           preferring1and2, preferring1 - preferring1and2, preferring2, numUsers - preferring2);
   return 1.0 - 1.0 / (1.0 + logLikelihood);
 }
 /** Creates a possibly weighted AbstractSimilarity. */
 AbstractSimilarity(final DataModel dataModel, Weighting weighting, boolean centerData)
     throws TasteException {
   if (dataModel == null) {
     throw new IllegalArgumentException("dataModel is null");
   }
   this.dataModel = dataModel;
   this.weighted = weighting == Weighting.WEIGHTED;
   this.centerData = centerData;
   this.cachedNumItems = dataModel.getNumItems();
   this.cachedNumUsers = dataModel.getNumUsers();
   this.refreshHelper =
       new RefreshHelper(
           new Callable<Object>() {
             @Override
             public Object call() throws TasteException {
               cachedNumItems = dataModel.getNumItems();
               cachedNumUsers = dataModel.getNumUsers();
               return null;
             }
           });
   this.refreshHelper.addDependency(this.dataModel);
 }
  @Override
  public double evaluate(
      RecommenderBuilder recommenderBuilder,
      DataModelBuilder dataModelBuilder,
      DataModel dataModel,
      double trainingPercentage,
      double evaluationPercentage)
      throws TasteException {
    Preconditions.checkNotNull(recommenderBuilder);
    Preconditions.checkNotNull(dataModel);
    Preconditions.checkArgument(
        trainingPercentage >= 0.0 && trainingPercentage <= 1.0,
        "Invalid trainingPercentage: "
            + trainingPercentage
            + ". Must be: 0.0 <= trainingPercentage <= 1.0");
    Preconditions.checkArgument(
        evaluationPercentage >= 0.0 && evaluationPercentage <= 1.0,
        "Invalid evaluationPercentage: "
            + evaluationPercentage
            + ". Must be: 0.0 <= evaluationPercentage <= 1.0");

    log.info("Beginning evaluation using {} of {}", trainingPercentage, dataModel);

    int numUsers = dataModel.getNumUsers();
    FastByIDMap<PreferenceArray> trainingPrefs =
        new FastByIDMap<PreferenceArray>(1 + (int) (evaluationPercentage * numUsers));
    FastByIDMap<PreferenceArray> testPrefs =
        new FastByIDMap<PreferenceArray>(1 + (int) (evaluationPercentage * numUsers));

    totalOfTrainingRatingsFromSource = 0;
    totalOfTrainingRatingsFromTargetWithContext = 0;
    totalOfTrainingRatingsFromTargetWithoutContext = 0;
    totalOfTestRatings = 0;

    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      if (random.nextDouble() < evaluationPercentage) {
        splitOneUsersPrefs(trainingPercentage, trainingPrefs, testPrefs, userID, dataModel);
      }
    }

    // System.out.println("Training (Source, TargetWithoutContext, TargetWithContext):
    // "+totalOfTrainingRatingsFromSource+"/"+totalOfTrainingRatingsFromTargetWithoutContext+"/"+totalOfTrainingRatingsFromTargetWithContext);
    // int totalTraining =
    // (totalOfTrainingRatingsFromSource+totalOfTrainingRatingsFromTargetWithContext+totalOfTrainingRatingsFromTargetWithoutContext);
    // System.out.println("Training/Test: "+totalTraining+"/"+totalOfTestRatings);

    DataModel newDataModel =
        dataModel instanceof ContextualDataModel
            ? new ContextualDataModel(trainingPrefs)
            : new GenericDataModel(trainingPrefs);

    DataModel trainingModel =
        dataModelBuilder == null ? newDataModel : dataModelBuilder.buildDataModel(trainingPrefs);

    Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);

    double result = getEvaluation(testPrefs, recommender);
    log.info("Evaluation result: {}", result);
    return result;
  }