Examples with Config edu.neu.ccs.pyramid.configuration.Config used on opensource projects

Example 66 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class CBMLR method reportGeneral.

private static void reportGeneral(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("computing other predictor-independent metrics");
    String output = config.getString("output.dir");
    File labelProbFile = Paths.get(output, "test_predictions", "label_probabilities.txt").toFile();
    double labelProbThreshold = config.getDouble("report.labelProbThreshold");
    try (BufferedWriter br = new BufferedWriter(new FileWriter(labelProbFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(CBMInspector.topLabels(cbm, dataSet.getRow(i), labelProbThreshold));
            br.newLine();
        }
    }
    System.out.println("individual label probabilities are saved to " + labelProbFile.getAbsolutePath());
    List<Integer> unobservedLabels = Arrays.stream(FileUtils.readFileToString(new File(output, "unobserved_labels.txt")).split(",")).map(s -> s.trim()).filter(s -> !s.isEmpty()).map(s -> Integer.parseInt(s)).collect(Collectors.toList());
    // Here we do not use approximation
    double[] logLikelihoods = IntStream.range(0, dataSet.getNumDataPoints()).parallel().mapToDouble(i -> cbm.predictLogAssignmentProb(dataSet.getRow(i), dataSet.getMultiLabels()[i])).toArray();
    double average = IntStream.range(0, dataSet.getNumDataPoints()).filter(i -> !containsNovelClass(dataSet.getMultiLabels()[i], unobservedLabels)).mapToDouble(i -> logLikelihoods[i]).average().getAsDouble();
    File logLikelihoodFile = Paths.get(output, "test_predictions", "ground_truth_log_likelihood.txt").toFile();
    FileUtils.writeStringToFile(logLikelihoodFile, PrintUtil.toMutipleLines(logLikelihoods));
    System.out.println("individual log likelihood of the test ground truth label set is written to " + logLikelihoodFile.getAbsolutePath());
    System.out.println("average log likelihood of the test ground truth label sets = " + average);
    if (!unobservedLabels.isEmpty()) {
        System.out.println("This is computed by ignoring test instances with new labels unobserved during training");
        System.out.println("The following labels do not actually appear in the training set and therefore cannot be learned:");
        System.out.println(ListUtil.toSimpleString(unobservedLabels));
    }
}

Example 67 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class CBMEN method reportHammingPrediction.

private static void reportHammingPrediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test set with the instance Hamming loss optimal predictor");
    String output = config.getString("output.dir");
    MarginalPredictor marginalPredictor = new MarginalPredictor(cbm);
    marginalPredictor.setPiThreshold(config.getDouble("predict.piThreshold"));
    MultiLabel[] predictions = marginalPredictor.predict(dataSet);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println("test performance with the instance Hamming loss optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_hamming_loss_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println("test performance is saved to " + performanceFile.toString());
    // Here we do not use approximation
    double[] setProbs = IntStream.range(0, predictions.length).parallel().mapToDouble(i -> cbm.predictAssignmentProb(dataSet.getRow(i), predictions[i])).toArray();
    File predictionFile = Paths.get(output, "test_predictions", "instance_hamming_loss_optimal", "predictions.txt").toFile();
    try (BufferedWriter br = new BufferedWriter(new FileWriter(predictionFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(predictions[i].toString());
            br.write(":");
            br.write("" + setProbs[i]);
            br.newLine();
        }
    }
    System.out.println("predicted sets and their probabilities are saved to " + predictionFile.getAbsolutePath());
    System.out.println("============================================================");
}

Example 68 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class CBMGB method reportGeneral.

private static void reportGeneral(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("computing other predictor-independent metrics");
    String output = config.getString("output.dir");
    File labelProbFile = Paths.get(output, "test_predictions", "label_probabilities.txt").toFile();
    double labelProbThreshold = config.getDouble("report.labelProbThreshold");
    try (BufferedWriter br = new BufferedWriter(new FileWriter(labelProbFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(CBMInspector.topLabels(cbm, dataSet.getRow(i), labelProbThreshold));
            br.newLine();
        }
    }
    System.out.println("individual label probabilities are saved to " + labelProbFile.getAbsolutePath());
    List<Integer> unobservedLabels = Arrays.stream(FileUtils.readFileToString(new File(output, "unobserved_labels.txt")).split(",")).map(s -> s.trim()).filter(s -> !s.isEmpty()).map(s -> Integer.parseInt(s)).collect(Collectors.toList());
    // Here we do not use approximation
    double[] logLikelihoods = IntStream.range(0, dataSet.getNumDataPoints()).parallel().mapToDouble(i -> cbm.predictLogAssignmentProb(dataSet.getRow(i), dataSet.getMultiLabels()[i])).toArray();
    double average = IntStream.range(0, dataSet.getNumDataPoints()).filter(i -> !containsNovelClass(dataSet.getMultiLabels()[i], unobservedLabels)).mapToDouble(i -> logLikelihoods[i]).average().getAsDouble();
    File logLikelihoodFile = Paths.get(output, "test_predictions", "ground_truth_log_likelihood.txt").toFile();
    FileUtils.writeStringToFile(logLikelihoodFile, PrintUtil.toMutipleLines(logLikelihoods));
    System.out.println("individual log likelihood of the test ground truth label set is written to " + logLikelihoodFile.getAbsolutePath());
    System.out.println("average log likelihood of the test ground truth label sets = " + average);
    if (!unobservedLabels.isEmpty()) {
        System.out.println("This is computed by ignoring test instances with new labels unobserved during training");
        System.out.println("The following labels do not actually appear in the training set and therefore cannot be learned:");
        System.out.println(ListUtil.toSimpleString(unobservedLabels));
    }
}

Example 69 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class App1 method loadAugmentedLabelTranslator.

static LabelTranslator loadAugmentedLabelTranslator(Config config, MultiLabelIndex index, String[] testIndexIds, LabelTranslator trainLabelTranslator, Logger logger) {
    File metaDataFolder = new File(config.getString("output.folder"), "meta_data");
    Config savedConfig = new Config(new File(metaDataFolder, "saved_config_app1"));
    List<String> extLabels = new ArrayList<>();
    for (int i = 0; i < trainLabelTranslator.getNumClasses(); i++) {
        extLabels.add(trainLabelTranslator.toExtLabel(i));
    }
    Collection<Terms.Bucket> buckets = index.termAggregation(savedConfig.getString("train.label.field"), testIndexIds);
    if (savedConfig.getBoolean("train.label.filter")) {
        String prefix = savedConfig.getString("train.label.filter.prefix");
        buckets = buckets.stream().filter(bucket -> bucket.getKey().startsWith(prefix)).collect(Collectors.toList());
    }
    List<String> newLabels = new ArrayList<>();
    logger.info("label distribution in data set:");
    StringBuilder stringBuilder = new StringBuilder();
    for (Terms.Bucket bucket : buckets) {
        stringBuilder.append(bucket.getKey());
        stringBuilder.append(":");
        stringBuilder.append(bucket.getDocCount());
        stringBuilder.append(", ");
        if (!extLabels.contains(bucket.getKey())) {
            extLabels.add(bucket.getKey());
            newLabels.add(bucket.getKey());
        }
    }
    logger.info(stringBuilder.toString());
    if (!newLabels.isEmpty()) {
        logger.warning("found new labels in data set: " + newLabels);
    }
    return new LabelTranslator(extLabels);
}

Example 70 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class ArffFormat method writeConfigFile.

private static void writeConfigFile(RegDataSet dataSet, File arffFile) {
    File configFile = new File(arffFile, ARFF_CONFIG_FILE_NAME);
    Config config = new Config();
    config.setInt(ARFF_CONFIG_NUM_DATA_POINTS, dataSet.getNumDataPoints());
    config.setInt(ARFF_CONFIG_NUM_FEATURES, dataSet.getNumFeatures());
    try {
        config.store(configFile);
    } catch (Exception e) {
        e.printStackTrace();
    }
}