org.apache.mahout.cf.taste.model.DataModel Java Examples

@Test
public void testBuildDefaultRecommender() {

    RecommenderBuilder rb = new GenericRecommenderBuilder();
    FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();
    userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
            new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
            new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));
    DataModel dm = new GenericDataModel(userData);

    Recommender rec = null;
    try {
        rec = rb.buildRecommender(dm);
    } catch (TasteException e) {
        e.printStackTrace();
    }
    assertTrue(rec instanceof RandomRecommender);
}
 

@Test
public void testBuildKNNRecommender() {
    GenericRecommenderBuilder rb = new GenericRecommenderBuilder();
    FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();
    userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
            new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
            new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));
    DataModel dm = new GenericDataModel(userData);

    Recommender rec = null;
    String recommenderType = "org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender";
    String similarityType = "org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity";
    try {
        rec = rb.buildRecommender(dm, recommenderType, similarityType);
    } catch (RecommenderException e) {
        e.printStackTrace();
    }
    assertTrue(rec instanceof GenericUserBasedRecommender);
}
 

private static void recommend(String ratingsFile, int ... userIds)
    throws TasteException, IOException {
  DataModel model = new FileDataModel(new File(ratingsFile));

  UserSimilarity similarity = new PearsonCorrelationSimilarity(model);

  UserNeighborhood neighborhood =
      new NearestNUserNeighborhood(
          100, similarity, model);

  Recommender recommender =  new GenericUserBasedRecommender(
      model, neighborhood, similarity);

  Recommender cachingRecommender = new CachingRecommender(recommender);

  for(int userId: userIds) {
    System.out.println("UserID " + userId);
    List<RecommendedItem> recommendations =
        cachingRecommender.recommend(userId, 2);
    for(RecommendedItem item: recommendations) {
      System.out.println("  item " + item.getItemID() + " score " + item.getValue());
    }
  }
}
 

public DataModel loadInMemory() {
	// In-memory DataModel - GenericDataModels
	FastByIDMap<PreferenceArray> preferences = new FastByIDMap<PreferenceArray>();

	PreferenceArray prefsForUser1 = new GenericUserPreferenceArray(10);
	prefsForUser1.setUserID(0, 1L);
	prefsForUser1.setItemID(0, 101L);
	prefsForUser1.setValue(0, 3.0f);
	prefsForUser1.setItemID(1, 102L);
	prefsForUser1.setValue(1, 4.5F);
	preferences.put(1L, prefsForUser1); // use userID as the key
	
	//TODO: add others users

	
	// Return preferences as new data model
	DataModel dataModel = new GenericDataModel(preferences);
	
	return dataModel;

}
 

public static void main( String[] args ) throws IOException, TasteException
{
	//user based recommender model
	DataModel model = new FileDataModel(new File("data/dataset.csv"));    	
	UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
	UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
	UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
	List<RecommendedItem> recommendations = recommender.recommend(2, 3);
	for (RecommendedItem recommendation : recommendations) {
	  System.out.println(recommendation);
	}
}
 

/**
 * Runs the recommender using the provided datamodels.
 *
 * @param opts see
 * {@link net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS}
 * @param trainingModel model to be used to train the recommender.
 * @param testModel model to be used to test the recommender.
 * @return see
 * {@link #runMahoutRecommender(net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS, org.apache.mahout.cf.taste.model.TemporalDataModelIF, org.apache.mahout.cf.taste.model.TemporalDataModelIF)}
 * @throws RecommenderException see null     {@link #runMahoutRecommender(net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS,
 * org.apache.mahout.cf.taste.model.DataModel, org.apache.mahout.cf.taste.model.DataModel)}
 * @throws TasteException see null     {@link #runMahoutRecommender(net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS,
 * org.apache.mahout.cf.taste.model.DataModel, org.apache.mahout.cf.taste.model.DataModel)}
 */
@Override
public TemporalDataModelIF<Long, Long> run(final RUN_OPTIONS opts,
        final net.recommenders.rival.core.TemporalDataModelIF<Long, Long> trainingModel,
        final net.recommenders.rival.core.TemporalDataModelIF<Long, Long> testModel)
        throws RecommenderException, TasteException {
    if (isAlreadyRecommended()) {
        return null;
    }
    // transform from core's DataModels to Mahout's DataModels
    DataModel trainingModelMahout = new DataModelWrapper(trainingModel);
    DataModel testModelMahout = new DataModelWrapper(testModel);

    return runMahoutRecommender(opts, trainingModelMahout, testModelMahout);
}
 

@Override
public Recommender buildRecommender(DataModel model)
    throws TasteException {
  UserSimilarity similarity =
      new PearsonCorrelationSimilarity(model);

  UserNeighborhood neighborhood =
      new NearestNUserNeighborhood(
          100,
          similarity, model);

  return new GenericUserBasedRecommender(
      model, neighborhood, similarity);
}
 

public static void evaluate(String ratingsFile)
    throws TasteException, IOException {
  DataModel model = new FileDataModel(new File(ratingsFile));
  RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
  RecommenderBuilder recommenderBuilder = new MyRecommendBuilder();
  evaluator.evaluate(
      recommenderBuilder,
      null,
      model,
      0.95,
      0.05
  );
}
 

public Recommender buildRecommender(DataModel arg0) {
	try {
		return BookRecommender.itemBased();
	} catch (Exception e) {
		// TODO Auto-generated catch block
		e.printStackTrace();
	}
	return null;
}
 

public static DataModel loadFromDB() throws Exception {
	
	// Database-based DataModel - MySQLJDBCDataModel
	/*
	 * A JDBCDataModel backed by a PostgreSQL database and accessed via
	 * JDBC. It may work with other JDBC databases. By default, this class
	 * assumes that there is a DataSource available under the JNDI name
	 * "jdbc/taste", which gives access to a database with a
	 * "taste_preferences" table with the following schema: CREATE TABLE
	 * taste_preferences ( user_id BIGINT NOT NULL, item_id BIGINT NOT NULL,
	 * preference REAL NOT NULL, PRIMARY KEY (user_id, item_id) ) CREATE
	 * INDEX taste_preferences_user_id_index ON taste_preferences (user_id);
	 * CREATE INDEX taste_preferences_item_id_index ON taste_preferences
	 * (item_id);
	 */
	MysqlDataSource dbsource = new MysqlDataSource();
	dbsource.setUser("user");
	dbsource.setPassword("pass");
	dbsource.setServerName("localhost");
	dbsource.setDatabaseName("my_db");

	DataModel dataModelDB = new MySQLJDBCDataModel(dbsource,
			"taste_preferences", "user_id", "item_id", "preference",
			"timestamp");
	
	return dataModelDB;
}
 

public static DataModel loadFromFile(String filePath) throws IOException{
	// File-based DataModel - FileDataModel
	DataModel dataModel = new FileDataModel(new File("preferences.csv"));
	return dataModel;

}
 

public static void main(String[] args) throws TasteException, IOException {
	DataModel model = new FileDataModel(new File("data/dataset.csv"));
   	ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
   	//UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
   	GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity);
   	List<RecommendedItem> recommendations = recommender.mostSimilarItems(18, 3);
   	for (RecommendedItem recommendation : recommendations) {
   	  System.out.println(recommendation);
   	}

}
 

public static void main(String[] args) throws TasteException, IOException {
	//MF recommender model
   	DataModel model = new FileDataModel(new File("data/recodataset.csv"));   
   	//ALSWRFactorizer factorizer = new ALSWRFactorizer(model, 50, 0.065, 15);
   	ParallelSGDFactorizer factorizer = new ParallelSGDFactorizer(model,10,0.1,1);
   	SVDRecommender recommender = new SVDRecommender(model, factorizer);    	
   	
   	List<RecommendedItem> recommendations = recommender.recommend(2, 3);
   	for (RecommendedItem recommendation : recommendations) {
   	  System.out.println(recommendation);
   	}

}
 

/**
 * Runs a Mahout recommender using the provided datamodels and the
 * previously provided properties.
 *
 * @param opts see
 * {@link net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS}
 * @param trainingModel model to be used to train the recommender.
 * @param testModel model to be used to test the recommender.
 * @return nothing when opts is
 * {@link net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS#OUTPUT_RECS},
 * otherwise, when opts is
 * {@link net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS#RETURN_RECS}
 * or
 * {@link net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS#RETURN_AND_OUTPUT_RECS}
 * it returns the predictions
 * @throws TasteException when there is a problem with the Mahout
 * recommender
 * @throws RecommenderException when recommender cannot be instantiated
 * properly
 */
public TemporalDataModelIF<Long, Long> runMahoutRecommender(final RUN_OPTIONS opts, final DataModel trainingModel, final DataModel testModel)
        throws RecommenderException, TasteException {
    if (isAlreadyRecommended()) {
        return null;
    }

    GenericRecommenderBuilder grb = new GenericRecommenderBuilder();

    if (getProperties().containsKey(RecommendationRunner.NEIGHBORHOOD) && getProperties().getProperty(RecommendationRunner.NEIGHBORHOOD).equals("-1")) {
        getProperties().setProperty(RecommendationRunner.NEIGHBORHOOD, Math.round(Math.sqrt(trainingModel.getNumItems())) + "");
    }
    if (getProperties().containsKey(RecommendationRunner.FACTORS) && getProperties().getProperty(RecommendationRunner.FACTORS).equals("-1")) {
        getProperties().setProperty(RecommendationRunner.FACTORS, Math.round(Math.sqrt(trainingModel.getNumItems())) + "");
    }

    Recommender recommender = null;
    if (getProperties().getProperty(RecommendationRunner.FACTORS) == null) {
        recommender = grb.buildRecommender(
                trainingModel,
                getProperties().getProperty(RecommendationRunner.RECOMMENDER),
                getProperties().getProperty(RecommendationRunner.SIMILARITY),
                Integer.parseInt(getProperties().getProperty(RecommendationRunner.NEIGHBORHOOD)));
    }
    if (getProperties().getProperty(RecommendationRunner.FACTORS) != null) {
        recommender = grb.buildRecommender(
                trainingModel,
                getProperties().getProperty(RecommendationRunner.RECOMMENDER),
                getProperties().getProperty(RecommendationRunner.FACTORIZER),
                DEFAULT_ITERATIONS,
                Integer.parseInt(getProperties().getProperty(RecommendationRunner.FACTORS)));
    }

    LongPrimitiveIterator users = testModel.getUserIDs();

    TemporalDataModelIF<Long, Long> model = null;
    switch (opts) {
        case RETURN_AND_OUTPUT_RECS:
        case RETURN_RECS:
            model = new TemporalDataModel<>();
            break;
        default:
            model = null;
    }
    String name = null;
    switch (opts) {
        case RETURN_AND_OUTPUT_RECS:
        case OUTPUT_RECS:
            name = getFileName();
            break;
        default:
            name = null;
    }
    boolean createFile = true;
    while (users.hasNext()) {
        long u = users.nextLong();
        try {
            List<RecommendedItem> items = recommender.recommend(u, trainingModel.getNumItems());
            //
            List<RecommenderIO.Preference<Long, Long>> prefs = new ArrayList<>();
            for (RecommendedItem i : items) {
                prefs.add(new RecommenderIO.Preference<>(u, i.getItemID(), i.getValue()));
            }
            //
            RecommenderIO.writeData(u, prefs, getPath(), name, !createFile, model);
            createFile = false;
        } catch (TasteException e) {
            e.printStackTrace();
        }
    }
    return model;
}
 

/**
 * @throws UnsupportedOperationException
 * @deprecated do not call
 */
@Deprecated
@Override
public DataModel getDataModel() {
  throw new UnsupportedOperationException();
}
 

public static void main(String[] args) throws IOException {
	DataModel model = new FileDataModel(new File("data/dataset.csv"));  
	Recommender recommender = (Recommender) new SlopeOneRecommender();

}
 

/**
 * Runs the recommender using models from file.
 *
 * @param opts see
 * {@link net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS}
 * @return see
 * {@link #runMahoutRecommender(net.recommenders.rival.recommend.frameworks.AbstractRunner.RUN_OPTIONS, org.apache.mahout.cf.taste.model.DataModel, org.apache.mahout.cf.taste.model.DataModel)}
 *
 * @throws RecommenderException when the recommender is instantiated
 * incorrectly.
 * @throws IOException when paths in property object are incorrect..
 * @throws TasteException when the recommender is instantiated incorrectly
 * or breaks otherwise.
 */
@Override
public TemporalDataModelIF<Long, Long> run(final RUN_OPTIONS opts) throws RecommenderException, TasteException, IOException {
    if (isAlreadyRecommended()) {
        return null;
    }
    DataModel trainingModel = new FileDataModel(new File(getProperties().getProperty(RecommendationRunner.TRAINING_SET)));
    DataModel testModel = new FileDataModel(new File(getProperties().getProperty(RecommendationRunner.TEST_SET)));
    return runMahoutRecommender(opts, trainingModel, testModel);
}
 

public OnlineRecommendation() throws IOException {

		DataModel model = new StringItemIdFileDataModel(new File("data/chap6/BX-Book-Ratings.csv"), ";");
		PlusAnonymousConcurrentUserDataModel plusModel = new PlusAnonymousConcurrentUserDataModel(model, concurrentUsers);
		// recommender = ...;

	}
 

/**
 * @deprecated do not call; only present to satisfy Mahout API
 * @throws UnsupportedOperationException in general
 */
@Deprecated
@Override
DataModel getDataModel();
 

/**
 * Not available. The client does not directly use any {@link DataModel}.
 *
 * @throws UnsupportedOperationException
 * @deprecated do not call
 */
@Deprecated
@Override
public DataModel getDataModel() {
  throw new UnsupportedOperationException();
}
 

/**
 * SVD.
 *
 * @param dataModel the data model
 * @param recType the recommender type (as Mahout class)
 * @param facType the factorizer (as Mahout class)
 * @param iterations number of iterations
 * @param factors number of factors
 * @return the recommender
 * @throws RecommenderException see
 * {@link #buildRecommender(org.apache.mahout.cf.taste.model.DataModel, java.lang.String, java.lang.String, int, int, int, java.lang.String)}
 */
public Recommender buildRecommender(final DataModel dataModel, final String recType, final String facType, final int iterations, final int factors)
        throws RecommenderException {
    return buildRecommender(dataModel, recType, null, NO_N, factors, iterations, facType);
}
 

/**
 * Recommender based on given recType, simType and neighborhood type.
 *
 * @param dataModel the data model
 * @param recType the recommender type (as Mahout class)
 * @param simType the similarity type (as Mahout class)
 * @param nbSize the neighborhood size
 * @return the recommender
 * @throws RecommenderException see {@link #buildRecommender(org.apache.mahout.cf.taste.model.DataModel, java.lang.String, java.lang.String, int, int, int, java.lang.String)}
 */
public Recommender buildRecommender(final DataModel dataModel, final String recType, final String simType, final int nbSize)
        throws RecommenderException {
    return buildRecommender(dataModel, recType, simType, nbSize, NOFACTORS, NOITER, null);
}
 

/**
 * Recommender based on given recType and simType (with default parameters).
 *
 * @param dataModel the data model
 * @param recType the recommender type (as Mahout class)
 * @param simType the similarity type (as Mahout class)
 * @return the recommender
 * @throws RecommenderException see {@link #buildRecommender(org.apache.mahout.cf.taste.model.DataModel, java.lang.String, java.lang.String, int, int, int, java.lang.String)}
 */
public Recommender buildRecommender(final DataModel dataModel, final String recType, final String simType)
        throws RecommenderException {
    return buildRecommender(dataModel, recType, simType, DEFAULT_N, NOFACTORS, NOITER, null);
}
 

/**
 * CF recommender with default parameters.
 *
 * @param dataModel the data model
 * @param recType the recommender type (as Mahout class)
 * @return the recommender
 * @throws RecommenderException see {@link #buildRecommender(org.apache.mahout.cf.taste.model.DataModel, java.lang.String, java.lang.String, int, int, int, java.lang.String)}
 */
public Recommender buildRecommender(final DataModel dataModel, final String recType)
        throws RecommenderException {
    return buildRecommender(dataModel, recType, null, DEFAULT_N, NOFACTORS, NOITER, null);
}
 

/**
 * Builds a random recommender which will recommend items from the data
 * model passed as a parameter.
 *
 * @param dataModel the data model
 * @return the recommender
 * @throws TasteException when the recommender is instantiated incorrectly.
 */
@Override
public Recommender buildRecommender(final DataModel dataModel)
        throws TasteException {
    return new RandomRecommender(dataModel);
}
 

/**
 * Default constructor.
 *
 * @param dataModel the data model.
 */
public PopularityBasedRecommender(final DataModel dataModel) {
    super(dataModel);
}
 

/**
 * Constructor when a canidate item strategy is to be used.
 *
 * @param dataModel the data model
 * @param candidateItemsStrategy the strategy
 */
public PopularityBasedRecommender(final DataModel dataModel, final CandidateItemsStrategy candidateItemsStrategy) {
    super(dataModel, candidateItemsStrategy);
}