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Java SimpleParser類代碼示例

本文整理匯總了Java中net.recommenders.rival.core.SimpleParser類的典型用法代碼示例。如果您正苦於以下問題：Java SimpleParser類的具體用法？Java SimpleParser怎麽用？Java SimpleParser使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。


SimpleParser類屬於net.recommenders.rival.core包，在下文中一共展示了SimpleParser類的14個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。

示例1: getIndexMap

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Read a user/item mapping (user/item original value, user/item internal
 * id) from a file and return the maximum index number in that file.
 *
 * @param in The file with id mapping.
 * @param map The user/item mapping
 * @return The largest id number.
 * @throws IOException if file does not exist.
 */
public static long getIndexMap(final File in, final Map<String, Long> map) throws IOException {
    long id = 0;
    if (in.exists()) {
        BufferedReader br = SimpleParser.getBufferedReader(in);
        String line;
        while ((line = br.readLine()) != null) {
            String[] toks = line.split("\t");
            long i = Long.parseLong(toks[1]);
            map.put(toks[0], i);
            id = Math.max(i, id);
        }
        br.close();
    }
    return id + 1;
}
開發者ID:recommenders，項目名稱:rival，代碼行數:25，代碼來源:AbstractLastfmCelmaParser.java

示例2: evaluate

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Evaluates the recommendations generated in previous steps.
 *
 * @param splitPath path where splits have been stored
 * @param recPath path where recommendation files have been stored
 */
public static void evaluate(final String splitPath, final String recPath) {
  double ndcgRes = 0.0;
  double precisionRes = 0.0;
  double rmseRes = 0.0;
  int i = 0;
  File testFile = new File(splitPath + "test_" + i + ".csv");
  File recFile = new File(recPath + "recs_" + i + ".csv");
  DataModel<Long, Long> testModel = null;
  DataModel<Long, Long> recModel = null;
  try {
    testModel = new SimpleParser().parseData(testFile);
    recModel = new SimpleParser().parseData(recFile);
  } catch (IOException e) {
    e.printStackTrace();
  }
  NDCG<Long, Long> ndcg = new NDCG<Long, Long>(recModel, testModel, new int[]{AT});
  ndcg.compute();
  ndcgRes += ndcg.getValueAt(AT);

  RMSE<Long, Long> rmse = new RMSE<Long, Long>(recModel, testModel);
  rmse.compute();
  rmseRes += rmse.getValue();

  Precision<Long, Long> precision = new Precision<Long, Long>(recModel, testModel, REL_TH, new int[]{AT});
  precision.compute();
  precisionRes += precision.getValueAt(AT);

  System.out.println("[email protected]" + AT + ": " + ndcgRes);
  System.out.println("RMSE: " + rmseRes);
  System.out.println("[email protected]" + AT + ": " + precisionRes);
}
開發者ID:recommenders，項目名稱:tutorial，代碼行數:38，代碼來源:RandomValidation.java

示例3: evaluate

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Evaluates the recommendations generated in previous steps.
 *
 * @param nFolds number of folds
 * @param splitPath path where splits have been stored
 * @param recPath path where recommendation files have been stored
 */
public static void evaluate(final int nFolds, final String splitPath, final String recPath) {
  double ndcgRes = 0.0;
  double precisionRes = 0.0;
  double rmseRes = 0.0;
  for (int i = 0; i < nFolds; i++) {
    File testFile = new File(splitPath + "test_" + i + ".csv");
    File recFile = new File(recPath + "recs_" + i + ".csv");
    DataModel<Long, Long> testModel = null;
    DataModel<Long, Long> recModel = null;
    try {
      testModel = new SimpleParser().parseData(testFile);
      recModel = new SimpleParser().parseData(recFile);
    } catch (IOException e) {
      e.printStackTrace();
    }
    NDCG ndcg = new NDCG(recModel, testModel, new int[]{AT});
    ndcg.compute();
    ndcgRes += ndcg.getValueAt(AT);

    RMSE rmse = new RMSE(recModel, testModel);
    rmse.compute();
    rmseRes += rmse.getValue();

    Precision precision = new Precision(recModel, testModel, REL_TH, new int[]{AT});
    precision.compute();
    precisionRes += precision.getValueAt(AT);
  }
  System.out.println("[email protected]" + AT + ": " + ndcgRes / nFolds);
  System.out.println("RMSE: " + rmseRes / nFolds);
  System.out.println("[email protected]" + AT + ": " + precisionRes / nFolds);

}
開發者ID:recommenders，項目名稱:tutorial，代碼行數:40，代碼來源:CrossValidation.java

示例4: Evaluator

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Constructor for the evaluator.
 * @param testsetPath The path to the file containing the testset.
 * @param predictionsetPath The path to the file containing the predictions.
 * @throws IOException When the files at either @testsetPath or @predictionsetPath do not exist.
 */
public Evaluator(String testsetPath, String predictionsetPath) throws IOException{
    File testFile = new File(testsetPath);
    File predictionFile = new File(predictionsetPath);
    SimpleParser testParser = new SimpleParser();
    this.test = testParser.parseData(testFile, ",");
    this.predictions = testParser.parseData(predictionFile, ",");
    this.ndcg = new NDCG(predictions, test, new int[]{AT});
}
開發者ID:recsyschallenge，項目名稱:RSChallengeEval，代碼行數:15，代碼來源:Evaluator.java

示例5: run

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Runs a single evaluation metric.
 *
 * @param properties The properties of the strategy.
 * @throws IOException if recommendation file is not found or output cannot
 * be written (see {@link #generateOutput(net.recommenders.rival.core.DataModelIF, int[],
 * net.recommenders.rival.evaluation.metric.EvaluationMetric, java.lang.String, java.lang.Boolean, java.io.File, java.lang.Boolean, java.lang.Boolean)})
 * @throws ClassNotFoundException see {@link #instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws IllegalAccessException see {@link #instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws InstantiationException see {@link #instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws InvocationTargetException see {@link #instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws NoSuchMethodException see {@link #instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 */
@SuppressWarnings("unchecked")
public static void run(final Properties properties)
        throws IOException, ClassNotFoundException, IllegalAccessException, InstantiationException, InvocationTargetException, NoSuchMethodException {
    System.out.println("Parsing started: recommendation file");
    File recommendationFile = new File(properties.getProperty(PREDICTION_FILE));
    DataModelIF<Long, Long> predictions;
    EvaluationStrategy.OUTPUT_FORMAT recFormat;
    if (properties.getProperty(PREDICTION_FILE_FORMAT).equals(EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL.toString())) {
        recFormat = EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL;
    } else {
        recFormat = EvaluationStrategy.OUTPUT_FORMAT.SIMPLE;
    }
    switch (recFormat) {
        case SIMPLE:
            predictions = new SimpleParser().parseData(recommendationFile);
            break;
        case TRECEVAL:
            predictions = new TrecEvalParser().parseData(recommendationFile);
            break;
        default:
            throw new AssertionError();
    }
    System.out.println("Parsing finished: recommendation file");
    System.out.println("Parsing started: test file");
    File testFile = new File(properties.getProperty(TEST_FILE));
    DataModelIF<Long, Long> testModel = new SimpleParser().parseData(testFile);
    System.out.println("Parsing finished: test file");
    // read other parameters
    Boolean overwrite = Boolean.parseBoolean(properties.getProperty(OUTPUT_OVERWRITE, "false"));
    Boolean doAppend = Boolean.parseBoolean(properties.getProperty(OUTPUT_APPEND, "true"));
    Boolean perUser = Boolean.parseBoolean(properties.getProperty(METRIC_PER_USER, "false"));
    File resultsFile = new File(properties.getProperty(OUTPUT_FILE));
    // get metric
    EvaluationMetric<Long> metric = instantiateEvaluationMetric(properties, predictions, testModel);
    // get ranking cutoffs
    int[] rankingCutoffs = getRankingCutoffs(properties);
    // generate output
    generateOutput(testModel, rankingCutoffs, metric, metric.getClass().getSimpleName(), perUser, resultsFile, overwrite, doAppend);
}
開發者ID:recommenders，項目名稱:rival，代碼行數:53，代碼來源:EvaluationMetricRunner.java

示例6: parseTemporalData

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * {@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;
}
開發者ID:recommenders，項目名稱:rival，代碼行數:17，代碼來源:MovielensParser.java

示例7: evaluate

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Evaluates the recommendations generated in previous steps.
 *
 * @param nFolds number of folds
 * @param splitPath path where splits have been stored
 * @param recPath path where recommendation files have been stored
 */
public static void evaluate(final int nFolds, final String splitPath, final String recPath) {
    double ndcgRes = 0.0;
    double precisionRes = 0.0;
    double rmseRes = 0.0;
    for (int i = 0; i < nFolds; i++) {
        File testFile = new File(splitPath + "test_" + i + ".csv");
        File recFile = new File(recPath + "recs_" + i + ".csv");
        DataModelIF<Long, Long> testModel = null;
        DataModelIF<Long, Long> recModel = null;
        try {
            testModel = new SimpleParser().parseData(testFile);
            recModel = new SimpleParser().parseData(recFile);
        } catch (IOException e) {
            e.printStackTrace();
        }
        NDCG<Long, Long> ndcg = new NDCG<>(recModel, testModel, new int[]{AT});
        ndcg.compute();
        ndcgRes += ndcg.getValueAt(AT);

        RMSE<Long, Long> rmse = new RMSE<>(recModel, testModel);
        rmse.compute();
        rmseRes += rmse.getValue();

        Precision<Long, Long> precision = new Precision<>(recModel, testModel, REL_TH, new int[]{AT});
        precision.compute();
        precisionRes += precision.getValueAt(AT);
    }
    System.out.println("[email protected]" + AT + ": " + ndcgRes / nFolds);
    System.out.println("RMSE: " + rmseRes / nFolds);
    System.out.println("[email protected]" + AT + ": " + precisionRes / nFolds);

}
開發者ID:recommenders，項目名稱:rival，代碼行數:40，代碼來源:CrossValidatedMahoutKNNRecommenderEvaluator.java

示例8: evaluate

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Evaluates the recommendations generated in previous steps.
 *
 * @param splitPath path where splits have been stored
 * @param recPath path where recommendation files have been stored
 */
public static void evaluate(final String splitPath, final String recPath) {
    double ndcgRes = 0.0;
    double precisionRes = 0.0;
    double rmseRes = 0.0;

    int i = 0;
    File testFile = new File(splitPath + "test_" + i + ".csv");
    File recFile = new File(recPath + "recs_" + i + ".csv");
    DataModelIF<Long, Long> testModel = null;
    DataModelIF<Long, Long> recModel = null;
    try {
        testModel = new SimpleParser().parseData(testFile);
        recModel = new SimpleParser().parseData(recFile);
    } catch (IOException e) {
        e.printStackTrace();
    }
    NDCG<Long, Long> ndcg = new NDCG<Long, Long>(recModel, testModel, new int[]{AT});
    ndcg.compute();
    ndcgRes += ndcg.getValueAt(AT);

    RMSE<Long, Long> rmse = new RMSE<Long, Long>(recModel, testModel);
    rmse.compute();
    rmseRes += rmse.getValue();

    Precision<Long, Long> precision = new Precision<Long, Long>(recModel, testModel, REL_TH, new int[]{AT});
    precision.compute();
    precisionRes += precision.getValueAt(AT);

    System.out.println("[email protected]" + AT + ": " + ndcgRes);
    System.out.println("RMSE: " + rmseRes);
    System.out.println("[email protected]" + AT + ": " + precisionRes);
}
開發者ID:recommenders，項目名稱:rival，代碼行數:39，代碼來源:RandomSplitMahoutKNNRecommenderEvaluator.java

示例9: evaluate

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Evaluates the recommendations generated in previous steps.
 *
 * @param splitPath path where splits have been stored
 * @param recPath path where recommendation files have been stored
 */
public static void evaluate(final String splitPath, final String recPath) {
    double ndcgRes = 0.0;
    double precisionRes = 0.0;
    double rmseRes = 0.0;

    int i = 0;
    File testFile = new File(splitPath + "test_" + i + ".csv");
    File recFile = new File(recPath + "recs_" + i + ".csv");
    DataModelIF<Long, Long> testModel = null;
    DataModelIF<Long, Long> recModel = null;
    try {
        testModel = new SimpleParser().parseData(testFile);
        recModel = new SimpleParser().parseData(recFile);
    } catch (IOException e) {
        e.printStackTrace();
    }
    NDCG<Long, Long> ndcg = new NDCG<>(recModel, testModel, new int[]{AT});
    ndcg.compute();
    ndcgRes += ndcg.getValueAt(AT);

    RMSE<Long, Long> rmse = new RMSE<>(recModel, testModel);
    rmse.compute();
    rmseRes += rmse.getValue();

    Precision<Long, Long> precision = new Precision<>(recModel, testModel, REL_TH, new int[]{AT});
    precision.compute();
    precisionRes += precision.getValueAt(AT);

    System.out.println("[email protected]" + AT + ": " + ndcgRes);
    System.out.println("RMSE: " + rmseRes);
    System.out.println("[email protected]" + AT + ": " + precisionRes);
}
開發者ID:recommenders，項目名稱:rival，代碼行數:39，代碼來源:TemporalSplitMahoutKNNRecommenderEvaluator.java

示例10: evaluate

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Evaluates the recommendations generated in previous steps.
 *
 * @param splitPath path where splits have been stored
 * @param recPath path where recommendation files have been stored
 */
public static void evaluate(final String splitPath, final String recPath) {
    double ndcgRes = 0.0;
    double precisionRes = 0.0;
    double rmseRes = 0.0;
    int i = 0;
    File testFile = new File(splitPath + "test_" + i + ".csv");
    File recFile = new File(recPath + "recs_" + i + ".csv");
    DataModelIF<Long, Long> testModel = null;
    DataModelIF<Long, Long> recModel = null;
    try {
        testModel = new SimpleParser().parseData(testFile);
        recModel = new SimpleParser().parseData(recFile);
    } catch (IOException e) {
        e.printStackTrace();
    }
    NDCG<Long, Long> ndcg = new NDCG<>(recModel, testModel, new int[]{AT});
    ndcg.compute();
    ndcgRes += ndcg.getValueAt(AT);

    RMSE<Long, Long> rmse = new RMSE<>(recModel, testModel);
    rmse.compute();
    rmseRes += rmse.getValue();

    Precision<Long, Long> precision = new Precision<>(recModel, testModel, REL_TH, new int[]{AT});
    precision.compute();
    precisionRes += precision.getValueAt(AT);

    System.out.println("[email protected]" + AT + ": " + ndcgRes);
    System.out.println("RMSE: " + rmseRes);
    System.out.println("[email protected]" + AT + ": " + precisionRes);
}
開發者ID:recommenders，項目名稱:rival，代碼行數:38，代碼來源:RandomMahoutIBRecommenderEvaluator.java

示例11: run

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Runs multiple evaluation metrics.
 *
 * @param properties The properties of the strategy.
 * @throws IOException if test file or prediction file are not found or
 * output cannot be generated (see {@link net.recommenders.rival.core.Parser#parseData(java.io.File)}
 * and {@link EvaluationMetricRunner#generateOutput(net.recommenders.rival.core.DataModelIF, int[],
 * net.recommenders.rival.evaluation.metric.EvaluationMetric, java.lang.String, java.lang.Boolean, java.io.File, java.lang.Boolean, java.lang.Boolean)}).
 * @throws ClassNotFoundException see
 * {@link EvaluationMetricRunner#instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws IllegalAccessException see
 * {@link EvaluationMetricRunner#instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws InstantiationException see
 * {@link EvaluationMetricRunner#instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws InvocationTargetException see
 * {@link EvaluationMetricRunner#instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws NoSuchMethodException see
 * {@link EvaluationMetricRunner#instantiateEvaluationMetric(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 */
@SuppressWarnings("unchecked")
public static void run(final Properties properties)
        throws IOException, ClassNotFoundException, IllegalAccessException, InstantiationException, InvocationTargetException, NoSuchMethodException {
    EvaluationStrategy.OUTPUT_FORMAT recFormat;
    if (properties.getProperty(PREDICTION_FILE_FORMAT).equals(EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL.toString())) {
        recFormat = EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL;
    } else {
        recFormat = EvaluationStrategy.OUTPUT_FORMAT.SIMPLE;
    }

    System.out.println("Parsing started: test file");
    File testFile = new File(properties.getProperty(TEST_FILE));
    DataModelIF<Long, Long> testModel = new SimpleParser().parseData(testFile);
    System.out.println("Parsing finished: test file");

    File predictionsFolder = new File(properties.getProperty(PREDICTION_FOLDER));
    String predictionsPrefix = properties.getProperty(PREDICTION_PREFIX);
    Set<String> predictionFiles = new HashSet<>();
    getAllPredictionFiles(predictionFiles, predictionsFolder, predictionsPrefix);

    // read other parameters
    Boolean overwrite = Boolean.parseBoolean(properties.getProperty(OUTPUT_OVERWRITE, "false"));
    Boolean doAppend = Boolean.parseBoolean(properties.getProperty(OUTPUT_APPEND, "true"));
    Boolean perUser = Boolean.parseBoolean(properties.getProperty(METRIC_PER_USER, "false"));
    int[] rankingCutoffs = EvaluationMetricRunner.getRankingCutoffs(properties);
    // process info for each result file
    File resultsFolder = new File(properties.getProperty(OUTPUT_FOLDER));
    for (String file : predictionFiles) {
        File predictionFile = new File(predictionsPrefix + file);
        System.out.println("Parsing started: recommendation file");
        DataModelIF<Long, Long> predictions;
        switch (recFormat) {
            case SIMPLE:
                predictions = new SimpleParser().parseData(predictionFile);
                break;
            case TRECEVAL:
                predictions = new TrecEvalParser().parseData(predictionFile);
                break;
            default:
                throw new AssertionError();
        }
        System.out.println("Parsing finished: recommendation file");
        File resultsFile = new File(resultsFolder, "eval" + "__" + predictionFile.getName());

        // get metrics
        for (EvaluationMetric<Long> metric : instantiateEvaluationMetrics(properties, predictions, testModel)) {
            // generate output
            EvaluationMetricRunner.generateOutput(testModel, rankingCutoffs, metric, metric.getClass().getSimpleName(), perUser, resultsFile, overwrite, doAppend);
        }
    }
}
開發者ID:recommenders，項目名稱:rival，代碼行數:71，代碼來源:MultipleEvaluationMetricRunner.java

示例12: run

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Process the property file and runs the specified strategies on some data.
 *
 * @param properties The property file
 * @throws IOException when a file cannot be parsed
 * @throws ClassNotFoundException when {@link Class#forName(java.lang.String)}
 * fails
 * @throws IllegalAccessException when {@link java.lang.reflect.Constructor#newInstance(java.lang.Object[])}
 * fails
 * @throws InstantiationException when {@link java.lang.reflect.Constructor#newInstance(java.lang.Object[])}
 * fails
 * @throws InvocationTargetException when {@link java.lang.reflect.Constructor#newInstance(java.lang.Object[])}
 * fails
 * @throws NoSuchMethodException when {@link Class#getConstructor(java.lang.Class[])}
 * fails
 */
public static void run(final Properties properties)
        throws IOException, ClassNotFoundException, IllegalAccessException, InstantiationException, InvocationTargetException, NoSuchMethodException {
    // read splits
    System.out.println("Parsing started: training file");
    File trainingFile = new File(properties.getProperty(TRAINING_FILE));
    DataModelIF<Long, Long> trainingModel = new SimpleParser().parseData(trainingFile);
    System.out.println("Parsing finished: training file");
    System.out.println("Parsing started: test file");
    File testFile = new File(properties.getProperty(TEST_FILE));
    DataModelIF<Long, Long> testModel = new SimpleParser().parseData(testFile);
    System.out.println("Parsing finished: test file");
    // read other parameters
    File inputFile = new File(properties.getProperty(INPUT_FILE));
    Boolean overwrite = Boolean.parseBoolean(properties.getProperty(OUTPUT_OVERWRITE, "false"));
    File rankingFile = new File(properties.getProperty(OUTPUT_FILE));
    File groundtruthFile = new File(properties.getProperty(GROUNDTRUTH_FILE));
    EvaluationStrategy.OUTPUT_FORMAT format = null;
    if (properties.getProperty(OUTPUT_FORMAT).equals(EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL.toString())) {
        format = EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL;
    } else {
        format = EvaluationStrategy.OUTPUT_FORMAT.SIMPLE;
    }
    Double threshold = Double.parseDouble(properties.getProperty(RELEVANCE_THRESHOLD));
    String strategyClassName = properties.getProperty(STRATEGY);
    Class<?> strategyClass = Class.forName(strategyClassName);
    // get strategy
    EvaluationStrategy<Long, Long> strategy = null;
    if (strategyClassName.contains("RelPlusN")) {
        Integer number = Integer.parseInt(properties.getProperty(RELPLUSN_N));
        Long seed = Long.parseLong(properties.getProperty(RELPLUSN_SEED));
        strategy = new RelPlusN(trainingModel, testModel, number, threshold, seed);
    } else {
        Object strategyObj = strategyClass.getConstructor(DataModelIF.class, DataModelIF.class, double.class).newInstance(trainingModel, testModel, threshold);
        if (strategyObj instanceof EvaluationStrategy) {
            @SuppressWarnings("unchecked")
            EvaluationStrategy<Long, Long> strategyTemp = (EvaluationStrategy<Long, Long>) strategyObj;
            strategy = strategyTemp;
        }
    }
    // generate output
    generateOutput(testModel, inputFile, strategy, format, rankingFile, groundtruthFile, overwrite);
}
開發者ID:recommenders，項目名稱:rival，代碼行數:59，代碼來源:StrategyRunnerInfile.java

示例13: run

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Runs a single evaluation strategy.
 *
 * @param properties The properties of the strategy.
 * @throws IOException when a file cannot be parsed
 * @throws ClassNotFoundException see {@link #instantiateStrategy(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws IllegalAccessException see {@link #instantiateStrategy(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws InstantiationException see {@link #instantiateStrategy(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws InvocationTargetException see {@link #instantiateStrategy(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 * @throws NoSuchMethodException see {@link #instantiateStrategy(java.util.Properties, net.recommenders.rival.core.DataModelIF, net.recommenders.rival.core.DataModelIF)}
 */
public static void run(final Properties properties)
        throws IOException, ClassNotFoundException, IllegalAccessException, InstantiationException, InvocationTargetException, NoSuchMethodException {
    // read splits
    System.out.println("Parsing started: training file");
    File trainingFile = new File(properties.getProperty(TRAINING_FILE));
    DataModelIF<Long, Long> trainingModel = new SimpleParser().parseData(trainingFile);
    System.out.println("Parsing finished: training file");
    System.out.println("Parsing started: test file");
    File testFile = new File(properties.getProperty(TEST_FILE));
    DataModelIF<Long, Long> testModel = new SimpleParser().parseData(testFile);
    System.out.println("Parsing finished: test file");
    // read other parameters
    File inputFile = new File(properties.getProperty(INPUT_FILE));
    Boolean overwrite = Boolean.parseBoolean(properties.getProperty(OUTPUT_OVERWRITE, "false"));
    File rankingFile = new File(properties.getProperty(OUTPUT_FILE));
    File groundtruthFile = new File(properties.getProperty(GROUNDTRUTH_FILE));
    EvaluationStrategy.OUTPUT_FORMAT format = null;
    if (properties.getProperty(OUTPUT_FORMAT).equals(EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL.toString())) {
        format = EvaluationStrategy.OUTPUT_FORMAT.TRECEVAL;
    } else {
        format = EvaluationStrategy.OUTPUT_FORMAT.SIMPLE;
    }

    // get strategy
    EvaluationStrategy<Long, Long> strategy = instantiateStrategy(properties, trainingModel, testModel);

    // read recommendations: user \t item \t score
    final Map<Long, List<Pair<Long, Double>>> mapUserRecommendations = new HashMap<Long, List<Pair<Long, Double>>>();
    BufferedReader in = new BufferedReader(new InputStreamReader(new FileInputStream(inputFile), "UTF-8"));
    try {
        String line = null;
        while ((line = in.readLine()) != null) {
            StrategyIO.readLine(line, mapUserRecommendations);
        }
    } finally {
        in.close();
    }
    // generate output
    generateOutput(testModel, mapUserRecommendations, strategy, format, rankingFile, groundtruthFile, overwrite);
}
開發者ID:recommenders，項目名稱:rival，代碼行數:52，代碼來源:StrategyRunner.java

示例14: evaluate

import net.recommenders.rival.core.SimpleParser; //導入依賴的package包/類
/**
 * Evaluate predictions using an evaluation model against the test set.
 *
 * @param splitPath         path where splits have been stored
 * @param strategyModelPath path where strategy model files have been stored
 * @return evaluation metrics
 */
public EvaluationMetrics evaluate(final String splitPath, final String strategyModelPath) throws IOException {

   EvaluationMetrics results = new EvaluationMetrics();
   double rmseResult = 0.0;
   double maeResult = 0.0;

   for (int cutoff : this.cutoffs) {
      double ndcgRes = 0.0;
      double precisionRes = 0.0;
      double recallRes = 0.0;

      for (int i = 0; i < this.numFolds; i++) {
         File testFile = new File(Paths.get(splitPath, "test_" + i + FILE_EXT).toString());
         File strategyFile = new File(Paths.get(strategyModelPath, "strategymodel_" + i + FILE_EXT).toString());
         DataModelIF<Long, Long> testModel = new SimpleParser().parseData(testFile);
         DataModelIF<Long, Long> strategyModel = new SimpleParser().parseData(strategyFile);

         // Error metrics calculated only once per fold, using all predictions
         if (cutoff == this.cutoffs[0]) {
            RMSE<Long, Long> rmse = new RMSE<>(strategyModel, testModel);
            rmse.compute();
            rmseResult += rmse.getValue();

            MAE<Long, Long> mae = new MAE<>(strategyModel, testModel);
            mae.compute();
            maeResult += mae.getValue();
         }

         // Ranking metrics
         NDCG<Long, Long> ndcg = new NDCG<>(strategyModel, testModel, new int[]{cutoff});
         ndcg.compute();
         ndcgRes += ndcg.getValueAt(cutoff);

         Precision<Long, Long> precision = new Precision<>(strategyModel, testModel, this.relevanceThreshold, new int[]{cutoff});
         precision.compute();
         precisionRes += precision.getValueAt(cutoff);

         Recall<Long, Long> recall = new Recall<>(strategyModel, testModel, this.relevanceThreshold, new int[]{cutoff});
         recall.compute();
         recallRes += recall.getValueAt(cutoff);
      }

      results.setPrecisionAtK(cutoff, precisionRes / this.numFolds);
      results.setRecallAtK(cutoff, recallRes / this.numFolds);
      results.setNDCGAtK(cutoff, ndcgRes / this.numFolds);

      log.info("Ranking metrics at {} computed", cutoff);
      log.info("[email protected]" + cutoff + ": " + ndcgRes / this.numFolds + " / [email protected]" + cutoff + ": " + precisionRes / this.numFolds
              + " / [email protected]" + cutoff + ": " + recallRes / this.numFolds);

   }

   results.setMAE(maeResult / this.numFolds);
   results.setRMSE(rmseResult / this.numFolds);
   log.info("Error metrics computed");
   log.info("RMSE: " + rmseResult / this.numFolds + " / MAE: " + maeResult / this.numFolds);

   return results;
}
開發者ID:recommenders，項目名稱:rival，代碼行數:67，代碼來源:CrossValidationRecSysEvaluator.java


注：本文中的net.recommenders.rival.core.SimpleParser類示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。