/** * {@inheritDoc} */ @Override public DataModelIF<Long, Long> parseData(final File f) throws IOException { DataModelIF<Long, Long> dataset = DataModelFactory.getDefaultModel(); BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(f), "UTF-8")); try { String line = null; while ((line = br.readLine()) != null) { parseLine(line, dataset); } } finally { br.close(); } return dataset; }
/** * {@inheritDoc} */ @Override public TemporalDataModelIF<Long, Long> parseTemporalData(final File f) throws IOException { TemporalDataModelIF<Long, Long> dataset = DataModelFactory.getDefaultTemporalModel(); BufferedReader br = SimpleParser.getBufferedReader(f); String line; while ((line = br.readLine()) != null) { parseLine(line, dataset); } br.close(); return dataset; }
public static <U, I> DataModelIF<U, I> getDefaultModel() { return getSimpleModel(); }
public static <U, I> TemporalDataModelIF<U, I> getDefaultTemporalModel() { return getSimpleTemporalModel(); }
@SuppressWarnings("unchecked") final DataModelIF<U, I>[] splits = new DataModelIF[2]; splits[0] = DataModelFactory.getDefaultModel(); // training splits[1] = DataModelFactory.getDefaultModel(); // test if (perUser) { for (U user : data.getUsers()) {
/** * Parses a data file with a specific separator between fields. * * @param f The file to be parsed. * @param token The separator to be used. * @param isTemporal A flag indicating if the file contains temporal * information. * @return A dataset created from the file. * @throws IOException if the file cannot be read. */ public TemporalDataModelIF<Long, Long> parseData(final File f, final String token, final boolean isTemporal) throws IOException { TemporalDataModelIF<Long, Long> dataset = DataModelFactory.getDefaultTemporalModel(); BufferedReader br = SimpleParser.getBufferedReader(f); String line = br.readLine(); if ((line != null) && (!line.matches(".*[a-zA-Z].*"))) { parseLine(line, dataset, token, isTemporal); } while ((line = br.readLine()) != null) { parseLine(line, dataset, token, isTemporal); } br.close(); return dataset; }
final DataModelIF<U, I>[] splits = new DataModelIF[2 * nFolds]; for (int i = 0; i < nFolds; i++) { splits[2 * i] = DataModelFactory.getDefaultModel(); // training splits[2 * i + 1] = DataModelFactory.getDefaultModel(); // test
TemporalDataModelIF<Long, Long> dataset = DataModelFactory.getDefaultTemporalModel();
for (EvaluationStrategy<Long, Long> strategy : MultipleStrategyRunner.instantiateStrategies(properties, trainingModel, testModel)) { DataModelIF<Long, Long> modelToEval = DataModelFactory.getDefaultModel(); for (Long user : recModel.getUsers()) { for (Long item : strategy.getCandidateItemsToRank(user)) {
TemporalDataModelIF<Long, Long> dataset = DataModelFactory.getDefaultTemporalModel();
DataModelIF<Long, Long> modelToEval = DataModelFactory.getDefaultModel(); for (Long user : recModel.getUsers()) { for (Long item : strategy.getCandidateItemsToRank(user)) {
final TemporalDataModelIF<U, I>[] splits = new TemporalDataModelIF[2 * nFolds]; for (int i = 0; i < nFolds; i++) { splits[2 * i] = DataModelFactory.getDefaultTemporalModel(); // training splits[2 * i + 1] = DataModelFactory.getDefaultTemporalModel(); // test
DataModelIF<Long, Long> modelToEval = DataModelFactory.getDefaultModel();
/** * {@inheritDoc} */ @Override public TemporalDataModelIF<Long, Long> parseTemporalData(final File f) throws IOException { TemporalDataModelIF<Long, Long> dataset = DataModelFactory.getDefaultTemporalModel(); Reader in = new InputStreamReader(new FileInputStream(f), "UTF-8"); Iterable<CSVRecord> records; if (isHasHeader()) { records = CSVFormat.EXCEL.withDelimiter(getDelimiter()).withHeader().parse(in); } else { records = CSVFormat.EXCEL.withDelimiter(getDelimiter()).parse(in); } for (CSVRecord record : records) { long userID = Long.parseLong(record.get(getUserTok())); long itemID = Long.parseLong(record.get(getItemTok())); long timestamp = -1L; if (getTimeTok() != -1) { timestamp = Long.parseLong(record.get(getTimeTok())); } double preference = Double.parseDouble(record.get(getPrefTok())); dataset.addPreference(userID, itemID, preference); dataset.addTimestamp(userID, itemID, timestamp); } in.close(); return dataset; }
DataModelIF<Long, Long> evaluationModel = DataModelFactory.getDefaultModel(); try { DataModelUtils.saveDataModel(evaluationModel, Paths.get(outPath, "strategymodel_" + i + FILE_EXT).toString(), true, "\t");
@SuppressWarnings("unchecked") final TemporalDataModelIF<U, I>[] splits = new TemporalDataModel[2]; splits[0] = DataModelFactory.getDefaultTemporalModel(); // training splits[1] = DataModelFactory.getDefaultTemporalModel(); // test if (perUser) { for (U user : data.getUsers()) {
DataModelIF<Long, Long> modelToEval = DataModelFactory.getDefaultModel(); for (Long user : recModel.getUsers()) { assert strategy != null;
DataModelIF<Long, Long> modelToEval = DataModelFactory.getDefaultModel(); for (Long user : recModel.getUsers()) { assert strategy != null;
DataModelIF<Long, Long> modelToEval = DataModelFactory.getDefaultModel(); for (Long user : recModel.getUsers()) { assert strategy != null;